Paper1 3D Change Localization and Captioning From Dynamic Scans of Indoor Scenes
摘要原文: Daily indoor scenes often involve constant changes due to human activities. To recognize scene changes, existing change captioning methods focus on describing changes from two images of a scene. However, to accurately perceive and appropriately evaluate physical changes and then identify the geometry of changed objects, recognizing and localizing changes in 3D space is crucial. Therefore, we propose a task to explicitly localize changes in 3D bounding boxes from two point clouds and describe detailed scene changes, including change types, object attributes, and spatial locations. Moreover, we create a simulated dataset with various scenes, allowing generating data without labor costs. We further propose a framework that allows different 3D object detectors to be incorporated in the change detection process, after which captions are generated based on the correlations of different change regions. The proposed framework achieves promising results in both change detection and captioning. Furthermore, we also evaluated on data collected from real scenes. The experiments show that pretraining on the proposed dataset increases the change detection accuracy by +12.8% (mAP0.25) when applied to real-world data. We believe that our proposed dataset and discussion could provide both a new benchmark and insights for future studies in scene change understanding.
Paper2 Text and Image Guided 3D Avatar Generation and Manipulation
摘要原文: The manipulation of latent space has recently become an interesting topic in the field of generative models. Recent research shows that latent directions can be used to manipulate images towards certain attributes. However, controlling the generation process of 3D generative models remains a challenge. In this work, we propose a novel 3D manipulation method that can manipulate both the shape and texture of the model using text or image-based prompts such as ‘a young face’ or ‘a surprised face’. We leverage the power of Contrastive Language-Image Pre-training (CLIP) model and a pre-trained 3D GAN model designed to generate face avatars, and create a fully differentiable rendering pipeline to manipulate meshes. More specifically, our method takes an input latent code and modifies it such that the target attribute specified by a text or image prompt is present or enhanced, while leaving other attributes largely unaffected. Our method requires only 5 minutes per manipulation, and we demonstrate the effectiveness of our approach with extensive results and comparisons.
Paper3 Controllable 3D Generative Adversarial Face Model via Disentangling Shape and Appearance
摘要原文: 3D face modeling has been an active area of research in computer vision and computer graphics, fueling applications ranging from facial expression transfer in virtual avatars to synthetic data generation. Existing 3D deep learning generative models (e.g., VAE, GANs) allow generating compact face representations (both shape and texture) that can model non-linearities in the shape and appearance space (e.g., scatter effects, specularities,…). However, they lack the capability to control the generation of subtle expressions. This paper proposes a new 3D face generative model that can decouple identity and expression and provides granular control over expressions. In particular, we propose using a pair of supervised auto-encoder and generative adversarial networks to produce high-quality 3D faces, both in terms of appearance and shape. Experimental results in the generation of 3D faces learned with holistic expression labels, or Action Unit (AU) labels, show how we can decouple identity and expression; gaining fine-control over expressions while preserving identity.
摘要原文: We focus on the task of far-field 3D detection (Far3Det) of objects beyond a certain distance from an observer, e.g., >50m. Far3Det is particularly important for autonomous vehicles (AVs) operating at highway speeds, which require detections of far-field obstacles to ensure sufficient braking distances. However, contemporary AV benchmarks such as nuScenes underemphasize this problem because they evaluate performance only up to a certain distance (50m). One reason is that obtaining far-field 3D annotations is difficult, particularly for lidar sensors that produce very few point returns for far-away objects. Indeed, we find that almost 50% of far-field objects (beyond 50m) contain zero lidar points. Secondly, current metrics for 3D detection employ a “one-size-fits-all” philosophy, using the same tolerance thresholds for near and far objects, inconsistent with tolerances for both human vision and stereo disparities. Both factors lead to an incomplete analysis of the Far3Det task. For example, while conventional wisdom tells us that high-resolution RGB sensors should be vital for the 3D detection of far-away objects, lidar-based methods still rank higher compared to RGB counterparts on the current benchmark leaderboards. As a first step towards a Far3Det benchmark, we develop a method to find well-annotated scenes from the nuScenes dataset and derive a well-annotated far-field validation set. We also propose a Far3Det evaluation protocol and explore various 3D detection methods for Far3Det. Our result convincingly justifies the long held conventional wisdom that high-resolution RGB improves 3D detection in the far-field. We further propose a simple yet effective method that fuses detections from RGB and lidar detectors based on non-maximum suppression, which remarkably outperforms state-of-the-art 3D detectors in the far-field.
Paper5 IDD-3D: Indian Driving Dataset for 3D Unstructured Road Scenes
摘要原文: Autonomous driving and assistance systems rely on annotated data from traffic and road scenarios to model and learn the various object relations in complex real-world scenarios. Preparation and training of deploy-able deep learning architectures require the models to be suited to different traffic scenarios and adapt to different situations. Currently, existing datasets, while large-scale, lack such diversities and are geographically biased towards mainly developed cities. An unstructured and complex driving layout found in several developing countries such as India poses a challenge to these models due to the sheer degree of variations in the object types, densities, and locations. To facilitate better research toward accommodating such scenarios, we build a new dataset, IDD-3D , which consists of multi-modal data from multiple cameras and LiDAR sensors with 12k annotated driving LiDAR frames across various traffic scenarios. We discuss the need for this dataset through statistical comparisons with existing datasets and highlight benchmarks on standard 3D object detection and tracking tasks in complex layouts.
Paper6 Real-Time Concealed Weapon Detection on 3D Radar Images for Walk-Through Screening System
摘要原文: This paper presents a framework for real-time concealed weapon detection (CWD) on 3D radar images for walk-through screening systems. The walk-through screening system aims to ensure security in crowded areas by performing CWD on walking persons, hence it requires an accurate and real-time detection approach. To ensure accuracy, a weapon needs to be detected irrespective of its 3D orientation, thus we use the 3D radar images as detection input. For achieving real-time, we reformulate classic U-Net based segmentation networks to perform 3D detection tasks. Our 3D segmentation network predicts peak-shaped probability map, instead of voxel-wise masks, to enable position inference by elementary peak detection operation on the predicted map. In the peak-shaped probability map, the peak marks the weapon’s position. So, weapon detection task translates to peak detection on the probability map. A Gaussian function is used to model weapons in the probability map. We experimentally validate our approach on realistic 3D radar images obtained from a walk-through weapon screening system prototype. Extensive ablation studies verify the effectiveness of our proposed approach over existing conventional approaches. The experimental results demonstrate that our proposed approach can perform accurate and real-time CWD, thus making it suitable for practical applications of walk-through screening.
Paper7 Automated Line Labelling: Dataset for Contour Detection and 3D Reconstruction
摘要原文: Understanding the finer details of a 3D object, its contours, is the first step toward a physical understanding of an object. Many real-world application domains require adaptable 3D object shape recognition models, usually with little training data. For this purpose, we develop the first automatically generated contour labeled dataset, bypassing manual human labeling. Using this dataset, we study the performance of current state-of-the-art instance segmentation algorithms on detecting and labeling the contours. We produce promising visual results with accurate contour prediction and labeling. We demonstrate that our finely labeled contours can help downstream tasks in computer vision, such as 3D reconstruction from a 2D image.
Paper8 TransPillars: Coarse-To-Fine Aggregation for Multi-Frame 3D Object Detection
摘要原文: 3D object detection using point clouds has attracted increasing attention due to its wide applications in autonomous driving and robotics. However, most existing studies focus on single point cloud frames without harnessing the temporal information in point cloud sequences. In this paper, we design TransPillars, a novel transformer-based feature aggregation technique that exploits temporal features of consecutive point cloud frames for multi-frame 3D object detection. TransPillars aggregates spatial-temporal point cloud features from two perspectives. First, it fuses voxel-level features directly from multi-frame feature maps instead of pooled instance features to preserve instance details with contextual information that are essential to accurate object localization. Second, it introduces a hierarchical coarse-to-fine strategy to fuse multi-scale features progressively to effectively capture the motion of moving objects and guide the aggregation of fine features. Besides, a variant of deformable transformer is introduced to improve the effectiveness of cross-frame feature matching. Extensive experiments show that our proposed TransPillars achieves state-of-art performance as compared to existing multi-frame detection approaches.
Paper9 Generative Range Imaging for Learning Scene Priors of 3D LiDAR Data
摘要原文: 3D LiDAR sensors are indispensable for the robust vision of autonomous mobile robots. However, deploying LiDAR-based perception algorithms often fails due to a domain gap from the training environment, such as inconsistent angular resolution and missing properties. Existing studies have tackled the issue by learning inter-domain mapping, while the transferability is constrained by the training configuration and the training is susceptible to peculiar lossy noises called ray-drop. To address the issue, this paper proposes a generative model of LiDAR range images applicable to the data-level domain transfer. Motivated by the fact that LiDAR measurement is based on point-by-point range imaging, we train an implicit image representation-based generative adversarial networks along with a differentiable ray-drop effect. We demonstrate the fidelity and diversity of our model in comparison with the point-based and image-based state-of-the-art generative models. We also showcase upsampling and restoration applications. Furthermore, we introduce a Sim2Real application for LiDAR semantic segmentation. We demonstrate that our method is effective as a realistic ray-drop simulator and outperforms state-of-the-art methods.
Paper10 Placing Human Animations Into 3D Scenes by Learning Interaction- and Geometry-Driven Keyframes
摘要原文: We present a novel method for placing a 3D human animation into a 3D scene while maintaining any human-scene interactions in the animation. We use the notion of computing the most important meshes in the animation for the interaction with the scene, which we call “keyframes.” These keyframes allow us to better optimize the placement of the animation into the scene such that interactions in the animations (standing, laying, sitting, etc.) match the affordances of the scene (e.g., standing on the floor or laying in a bed). We compare our method, which we call PAAK, with prior approaches, including POSA, PROX ground truth, and a motion synthesis method, and highlight the benefits of our method with a perceptual study. Human raters preferred our PAAK method over the PROX ground truth data 64.6% of the time. Additionally, in direct comparisons, the raters preferred PAAK over competing methods including 61.5% compared to POSA. Our project website is available at https://gamma.umd.edu/paak/.
Paper11 Dense Voxel Fusion for 3D Object Detection
摘要原文: Camera and LiDAR sensor modalities provide complementary appearance and geometric information useful for detecting 3D objects for autonomous vehicle applications. However, current end-to-end fusion methods are challenging to train and underperform state-of-the-art LiDAR-only detectors. Sequential fusion methods suffer from a limited number of pixel and point correspondences due to point cloud sparsity, or their performance is strictly capped by the detections of one of the modalities. Our proposed solution, Dense Voxel Fusion (DVF) is a sequential fusion method that generates multi-scale dense voxel feature representations, improving expressiveness in low point density regions. To enhance multi-modal learning, we train directly with projected ground truth 3D bounding box labels, avoiding noisy, detector-specific 2D predictions. Both DVF and the multi-modal training approach can be applied to any voxel-based LiDAR backbone. DVF ranks 3rd among published fusion methods on KITTI’s 3D car detection benchmark without introducing additional trainable parameters, nor requiring stereo images or dense depth labels. In addition, DVF significantly improves 3D vehicle detection performance of voxel-based methods on the Waymo Open Dataset.
Paper12 Class-Level Confidence Based 3D Semi-Supervised Learning
摘要原文: Current pseudo-labeling strategies in 3D semi-supervised learning (SSL) fail to dynamically incorporate the variance of learning status which is affected by each class’s learning difficulty and data imbalance. To address this problem, we practically demonstrate that 3D unlabeled data class-level confidence can represent the learning status. Based on this finding, we present a novel class-level confidence based 3D SSL method. Firstly, a dynamic thresholding strategy is proposed to utilize more unlabeled data, especially for low learning status classes. Then, a re-sampling strategy is designed to avoid biasing toward high learning status classes, which dynamically changes the sampling probability of each class. Unlike the latest state-of-the-art SSL method FlexMatch which also utilizes dynamic threshold, our method can be applied to the inherently imbalanced dataset and thus is more general. To show the effectiveness of our method in 3D SSL tasks, we conduct extensive experiments on 3D SSL classification and detection tasks. Our method significantly outperforms state-of-the-art counterparts for both 3D SSL classification and detection tasks in all datasets.
Paper13 PIDS: Joint Point Interaction-Dimension Search for 3D Point Cloud
摘要原文: The interaction and dimension of points are two important axes in designing point operators to serve hierarchical 3D models. Yet, these two axes are heterogeneous and challenging to fully explore. Existing works craft point operator under a single axis and reuse the crafted operator in all parts of 3D models. This overlooks the opportunity to better combine point interactions and dimensions by exploiting varying geometry/density of 3D point clouds. In this work, we establish PIDS, a novel paradigm to jointly explore point interactions and point dimensions to serve semantic segmentation on point cloud data. We establish a large search space to jointly consider versatile point interactions and point dimensions. This supports point operators with various geometry/density considerations. The enlarged search space with heterogeneous search components calls for a better ranking of candidate models. To achieve this, we improve the search space exploration by leveraging predictor-based Neural Architecture Search (NAS), and enhance the quality of prediction by assigning unique encoding to heterogeneous search components based on their priors. We thoroughly evaluate the networks crafted by PIDS on two semantic segmentation benchmarks, showing 1% mIOU improvement on SemanticKITTI and S3DIS over state-of-the-art 3D models.
Paper14 RSF: Optimizing Rigid Scene Flow From 3D Point Clouds Without Labels
摘要原文: We present a method for optimizing object-level rigid 3D scene flow over two successive point clouds without any annotated labels in autonomous driving settings. Rather than using pointwise flow vectors, our approach represents scene flow as the composition a global ego-motion and a set of bounding boxes with their own rigid motions, exploiting the multi-body rigidity commonly present in dynamic scenes. We jointly optimize these parameters over a novel loss function based on the nearest neighbor distance using a differentiable bounding box formulation. Our approach achieves state-of-the-art accuracy on KITTI Scene Flow and nuScenes without requiring any annotations, outperforming even supervised methods. Additionally, we demonstrate the effectiveness of our approach on motion segmentation and ego-motion estimation. Lastly, we visualize our predictions and validate our loss function design with an ablation study.
中文总结: 我们提出了一种在自动驾驶环境中优化两个连续点云上的物体级刚性3D场景流的方法，而无需任何标注标签。与使用逐点流向量不同，我们的方法将场景流表示为全局自我运动和一组带有自己刚性运动的边界框的组合，利用动态场景中常见的多体刚性。我们通过基于最近邻距离的新型损失函数联合优化这些参数，使用可微分的边界框公式。我们的方法在不需要任何注释的情况下，在KITTI Scene Flow和nuScenes上实现了最先进的准确性，甚至优于监督方法。此外，我们展示了我们的方法在运动分割和自我运动估计上的有效性。最后，我们通过消融研究可视化我们的预测并验证我们的损失函数设计。
Paper15 ImpDet: Exploring Implicit Fields for 3D Object Detection
摘要原文: Conventional 3D object detection approaches concentrate on bounding boxes representation learning with several parameters, i.e., localization, dimension, and orientation. Despite its popularity and universality, such a straightforward paradigm is sensitive to slight numerical deviations, especially in localization. By exploiting the property that point clouds are naturally captured on the surface of objects along with accurate location and intensity information, we introduce a new perspective that views bounding box regression as an implicit function. This leads to our proposed framework, termed Implicit Detection or ImpDet, which leverages implicit field learning for 3D object detection. Our ImpDet assigns specific values to points in different local 3D spaces, thereby high-quality boundaries can be generated by classifying points inside or outside the boundary. To solve the problem of sparsity on the object surface, we further present a simple yet efficient virtual sampling strategy to not only fill the empty region, but also learn rich semantic features to help refine the boundaries. Extensive experimental results on KITTI and Waymo benchmarks demonstrate the effectiveness and robustness of unifying implicit fields into object detection.
Paper16 Multivariate Probabilistic Monocular 3D Object Detection
摘要原文: In autonomous driving, monocular 3D object detection is an important but challenging task. Towards accurate monocular 3D object detection, some recent methods recover the distance of objects from the physical height and visual height of objects. Such decomposition framework can introduce explicit constraints on the distance prediction, thus improving its accuracy and robustness. However, the inaccurate physical height and visual height prediction still may exacerbate the inaccuracy of the distance prediction. In this paper, we improve the framework by multivariate probabilistic modeling. We explicitly model the joint probability distribution of the physical height and visual height. This is achieved by learning a full covariance matrix of the physical height and visual height during training, with the guide of a multivariate likelihood. Such explicit joint probability distribution modeling not only leads to robust distance prediction when both the predicted physical height and visual height are inaccurate, but also brings learned covariance matrices with expected behaviors. The experimental results on the challenging Waymo Open and KITTI datasets show the effectiveness of our framework.
Paper17 PointNeuron: 3D Neuron Reconstruction via Geometry and Topology Learning of Point Clouds
摘要原文: Digital neuron reconstruction from 3D microscopy images is an essential technique for investigating brain connectomics and neuron morphology. Existing reconstruction frameworks use convolution-based segmentation networks to partition the neuron from noisy backgrounds before applying the tracing algorithm. The tracing results are sensitive to the raw image quality and segmentation accuracy. In this paper, we propose a novel framework for 3D neuron reconstruction. Our key idea is to use the geometric representation power of the point cloud to better explore the intrinsic structural information of neurons. Our proposed framework adopts one graph convolutional network to predict the neural skeleton points and another one to produce the connectivity of these points. We finally generate the target SWC file through the interpretation of the predicted point coordinates, radius, and connections. Evaluated on the Janelia-Fly dataset from the BigNeuron project, we show that our framework achieves competitive neuron reconstruction performance. Our geometry and topology learning of point clouds could further benefit 3D medical image analysis, such as cardiac surface reconstruction. Our code is available at https://github.com/RunkaiZhao/PointNeuron.
Paper18 3D Neural Sculpting (3DNS): Editing Neural Signed Distance Functions
摘要原文: In recent years, implicit surface representations through neural networks that encode the signed distance have gained popularity and have achieved state-of-the-art results in various tasks (e.g. shape representation, shape reconstruction and learning shape priors). However, in contrast to conventional shape representations such as polygon meshes, the implicit representations cannot be easily edited and existing works that attempt to address this problem are extremely limited. In this work, we propose the first method for efficient interactive editing of signed distance functions expressed through neural networks, allowing free-form editing. Inspired by 3D sculpting software for meshes, we use a brush-based framework that is intuitive and can in the future be used by sculptors and digital artists. In order to localize the desired surface deformations, we regulate the network by using a copy of it to sample the previously expressed surface. We introduce a novel framework for simulating sculpting-style surface edits, in conjunction with interactive surface sampling and efficient adaptation of network weights. We qualitatively and quantitatively evaluate our method in various different 3D objects and under many different edits. The reported results clearly show that our method yields high accuracy, in terms of achieving the desired edits, while in the same time preserving the geometry outside the interaction areas.
Paper19 Learning To Detect 3D Lanes by Shape Matching and Embedding
摘要原文: 3D lane detection based on LiDAR point clouds is a challenging task that requires precise locations, accurate topologies, and distinguishable instances. In this paper, we propose a dual-level shape attention network (DSANet) with two branches for high-precision 3D lane predictions. Specifically, one branch predicts the refined lane segment shapes and the shape embeddings that encode the approximate lane instance shapes, the other branch detects the coarse-grained structures of the lane instances. In the training stage, two-level shape matching loss functions are introduced to jointly optimize the shape parameters of the two-branch outputs, which are simple yet effective for precision enhancement. Furthermore, a shape-guided segments aggregator is proposed to help local lane segments aggregate into complete lane instances, according to the differences of instance shapes predicted at different levels. Experiments conducted on our BEV-3DLanes dataset demonstrate that our method outperforms previous methods.
摘要原文: With the recent advances in NeRF-based 3D aware GANs quality, projecting an image into the latent space of these 3D-aware GANs has a natural advantage over 2D GAN inversion: not only does it allow multi-view consistent editing of the projected image, but it also enables 3D reconstruction and novel view synthesis when given only a single image. However, the explicit viewpoint control acts as a main hindrance in the 3D GAN inversion process, as both camera pose and latent code have to be optimized simultaneously to reconstruct the given image. Most works that explore the latent space of the 3D-aware GANs rely on ground-truth camera viewpoint or deformable 3D model, thus limiting their applicability. In this work, we introduce a generalizable 3D GAN inversion method that infers camera viewpoint and latent code simultaneously to enable multi-view consistent semantic image editing. The key to our approach is to leverage pre-trained estimators for better initialization and utilize the pixel-wise depth calculated from NeRF parameters to better reconstruct the given image. We conduct extensive experiments on image reconstruction and editing both quantitatively and qualitatively, and further compare our results with 2D GAN-based editing to demonstrate the advantages of utilizing the latent space of 3D GANs. Additional results and visualizations are available at https://hypernerf.github.io/.
Paper21 Li3DeTr: A LiDAR Based 3D Detection Transformer
摘要原文: Inspired by recent advances in vision transformers for object detection, we propose Li3DeTr, an end-to-end LiDAR based 3D Detection Transformer for autonomous driving, that inputs LiDAR point clouds and regresses 3D bounding boxes. The LiDAR local and global features are encoded using sparse convolution and multi-scale deformable attention respectively. In the decoder head, firstly, in the novel Li3DeTr cross-attention block, we link the LiDAR global features to 3D predictions leveraging the sparse set of object queries learnt from the data. Secondly, the object query interactions are formulated using multi-head self-attention. Finally, the decoder layer is repeated Ldec number of times to refine the object queries. Inspired by DETR, we employ set-to-set loss to train the Li3DeTr network. Without bells and whistles, the Li3DeTr network achieves 61.3% mAP and 67.6% NDS surpassing the state-of-the-art methods with non-maximum suppression (NMS) on the nuScenes dataset and it also achieves competitive performance on the KITTI dataset. We also employ knowledge distillation (KD) using a teacher and student model that slightly improves the performance of our network.
Paper22 CountNet3D: A 3D Computer Vision Approach To Infer Counts of Occluded Objects
摘要原文: 3D scene understanding is an important problem that has experienced great progress in recent years, in large part due to the development of state-of-the-art methods for 3D object detection. However, the performance of 3D object detectors can suffer in scenarios where extreme occlusion of objects is present, or the number of object classes is large. In this paper, we study the problem of inferring 3D counts from densely packed scenes with heterogeneous objects. This problem has applications to important tasks such as inventory management or automatic crop yield estimation. We propose a novel regression-based method, CountNet3D, that uses mature 2D object detectors for finegrained classification and localization, and a PointNet backbone for geometric embedding. The network processes fused data from images and point clouds for end-to-end learning of counts. We perform experiments on a novel synthetic dataset for inventory management in retail, which we construct and make publicly available to the community. Our results show that regression-based 3D counting methods systematically outperform detection-based methods, and reveal that directly learning from raw point clouds greatly assists count estimation under extreme occlusion. Finally, we study the effectiveness of CountNet3D on a large dataset of real-world scenes where extreme occlusion is present and achieve an error rate of 11.01%.
Paper23 3D-SpLineNet: 3D Traffic Line Detection Using Parametric Spline Representations
摘要原文: Monocular 3D traffic line detection jointly tackles the detection of lane markings and regression of their 3D location. The greatest challenge is the exact estimation of various line shapes in the world, which highly depends on the chosen representation. While anchor-based and grid-based line representations have been proposed, all suffer from the same limitation, the necessity of discretizing the 3D space. To address this limitation, we present an anchor-free parametric lane representation, which defines traffic lines as continuous curves in 3D space. Choosing splines as our representation, we show their superiority over polynomials of different degrees that were proposed in previous 2D lane detection approaches. Our continuous representation allows us to model even complex lane shapes at any position in the 3D space, while implicitly enforcing smoothness constraints. Our model is validated on a synthetic 3D lane dataset including a variety of scenes in terms of complexity of road shape and illumination. We outperform the state-of-the-art in nearly all geometric performance metrics and achieve a great leap in the detection rate. In contrast to discrete representations, our parametric model requires no post-processing achieving highest processing speed. Additionally, we provide a thorough analysis over different parametric representations for 3D lane detection. The code and trained models are available on our project website https://3d-splinenet.github.io/.
Paper24 Masked Image Modeling Advances 3D Medical Image Analysis
摘要原文: Recently, masked image modeling (MIM) has gained considerable attention due to its capacity to learn from vast amounts of unlabeled data and has been demonstrated to be effective on a wide variety of vision tasks involving natural images. Meanwhile, the potential of self-supervised learning in modeling 3D medical images is anticipated to be immense due to the high quantities of unlabeled images, and the expense and difficulty of quality labels. However, MIM’s applicability to medical images remains uncertain. In this paper, we demonstrate that masked image modeling approaches can also advance 3D medical images analysis in addition to natural images. We study how masked image modeling strategies leverage performance from the viewpoints of 3D medical image segmentation as a representative downstream task: i) when compared to naive contrastive learning, masked image modeling approaches accelerate the convergence of supervised training even faster (1.40x) and ultimately produce a higher dice score; ii) predicting raw voxel values with a high masking ratio and a relatively smaller patch size is non-trivial self-supervised pretext-task for medical images modeling; iii) a lightweight decoder or projection head design for reconstruction is powerful for masked image modeling on 3D medical images which speeds up training and reduce cost; iv) finally, we also investigate the effectiveness of MIM methods under different practical scenarios where different image resolutions and labeled data ratios are applied. Anonymized codes are available at https://anonymous.4open.science/r/MIM-Med3D.
Paper25 Visually Explaining 3D-CNN Predictions for Video Classification With an Adaptive Occlusion Sensitivity Analysis
摘要原文: This paper proposes a method for visually explaining the decision-making process of 3D convolutional neural networks (CNN) with a temporal extension of occlusion sensitivity analysis. The key idea here is to occlude a specific volume of data by a 3D mask in an input 3D temporal-spatial data space and then measure the change degree in the output score. The occluded volume data that produces a larger change degree is regarded as a more critical element for classification. However, while the occlusion sensitivity analysis is commonly used to analyze single image classification, it is not so straightforward to apply this idea to video classification as a simple fixed cuboid cannot deal with the motions. To this end, we adapt the shape of a 3D occlusion mask to complicated motions of target objects. Our flexible mask adaptation is performed by considering the temporal continuity and spatial co-occurrence of the optical flows extracted from the input video data. We further propose to approximate our method by using the first-order partial derivative of the score with respect to an input image to reduce its computational cost. We demonstrate the effectiveness of our method through various and extensive comparisons with the conventional methods in terms of the deletion/insertion metric and the pointing metric on the UCF-101. The code is available at: https://github.com/uchiyama33/AOSA.
Paper26 Learning Graph Variational Autoencoders With Constraints and Structured Priors for Conditional Indoor 3D Scene Generation
摘要原文: We present a graph variational autoencoder with a structured prior for generating the layout of indoor 3D scenes. Given the room type (e.g., living room or library) and the room layout (e.g., room elements such as floor and walls), our architecture generates a collection of objects (e.g., furniture items such as sofa, table and chairs) that is consistent with the room type and layout. This is a challenging problem because the generated scene needs to satisfy multiple constrains, e.g., each object should lie inside the room and two objects should not occupy the same volume. To address these challenges, we propose a deep generative model that encodes these relationships as soft constraints on an attributed graph (e.g., the nodes capture attributes of room and furniture elements, such as shape, class, pose and size, and the edges capture geometric relationships such as relative orientation). The architecture consists of a graph encoder that maps the input graph to a structured latent space, and a graph decoder that generates a furniture graph, given a latent code and the room graph. The latent space is modeled with autoregressive priors, which facilitates the generation of highly structured scenes. We also propose an efficient training procedure that combines matching and constrained learning. Experiments on the 3D-FRONT dataset show that our method produces scenes that are diverse and are adapted to the room layout.
摘要原文: Recent generative models based on neural radiance fields (NeRF) achieve the generation of diverse 3D-aware images. Despite the success, their applicability can be further expanded by incorporating with various types of user-specified conditions such as text and images. In this paper, we propose a novel approach called the conditional generative neural radiance fields (CG-NeRF), which generates multi-view images that reflect multimodal input conditions such as images or text. However, generating 3D-aware images from multimodal conditions bears several challenges. First, each condition type has different amount of information - e.g., the amount of information in text and color images are significantly different. Furthermore, the pose-consistency is often violated when diversifying the generated images from input conditions. Addressing such challenges, we propose 1) a unified architecture that effectively handles multiple types of conditions, and 2) the pose-consistent diversity loss for generating various images while maintaining the view consistency. Experimental results show that the proposed method maintains consistent image quality on various multimodal condition types and achieves superior fidelity and diversity compared to the existing NeRF-based generative models.
Paper28 3DMM-RF: Convolutional Radiance Fields for 3D Face Modeling
摘要原文: Facial 3D Morphable Models are a main computer vision subject with countless applications and have been highly optimized in the last two decades. The tremendous improvements of deep generative networks have created various possibilities for improving such models and have attracted wide interest. Moreover, the recent advances in neural radiance fields, are revolutionising novel-view synthesis of known scenes. In this work, we present a facial 3D Morphable Model, which exploits both of the above, and can accurately model a subject’s identity, pose and expression and render it in arbitrary illumination. This is achieved by utilizing a powerful deep style-based generator to overcome two main weaknesses of neural radiance fields, their rigidity and rendering speed. We introduce a style-based generative network that synthesizes in one pass all and only the required rendering samples of a neural radiance field. We create a vast labelled synthetic dataset of facial renders, and train the network, so that it can accurately model and generalize on facial identity, pose and appearance. Finally, we show that this model can accurately be fit to “in-the-wild” facial images of arbitrary pose and illumination, extract the facial characteristics, and be used to re-render the face in controllable conditions.
Paper29 Seg&Struct: The Interplay Between Part Segmentation and Structure Inference for 3D Shape Parsing
摘要原文: We propose Seg&Struct, a supervised learning framework leveraging the interplay between part segmentation and structure inference and demonstrating their synergy in an integrated framework. Both part segmentation and structure inference have been extensively studied in the recent deep learning literature, while the supervisions used for each task have not been fully exploited to assist the other task. Namely, structure inference has been typically conducted with an autoencoder that does not leverage the point-to-part associations. Also, segmentation has been mostly performed without structural priors that tell the plausibility of the output segments. We present how these two tasks can be best combined while fully utilizing supervision to improve performance. Our framework first decomposes a raw input shape into part segments using an off-the-shelf algorithm, whose outputs are then mapped to nodes in a part hierarchy, establishing point-to-part associations. Following this, ours predicts the structural information, e.g., part bounding boxes and part relationships. Lastly, the segmentation is rectified by examining the confusion of part boundaries using the structure-based part features. Our experimental results based on the StructureNet and PartNet demonstrate that the interplay between the two tasks results in remarkable improvements in both tasks: 27.91% in structure inference and 0.5% in segmentation.
Paper30 MonoEdge: Monocular 3D Object Detection Using Local Perspectives
摘要原文: We propose a novel approach for monocular 3D object detection by leveraging local perspective effects of each object. While the global perspective effect shown as size and position variations has been exploited for monocular 3D detection extensively, the local perspectives has long been overlooked. We propose a new regression target named keyedge-ratios as the parameterization of the local shape distortion to account for the local perspective, and derive the object depth and yaw angle from it. Theoretically, this approach does not rely on the absolute size or position of the objects in the image, therefore independent of the camera intrinsic parameters. This approach provides a new perspective for monocular 3D reasoning and can be plugged in flexibly to existing monocular 3D object detection frameworks. We demonstrate effectiveness and superior performance over strong baseline methods in multiple datasets.
