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检索条件"主题词=3D from multi-view and sensors"

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dynamicStereo: Consistent dynamic depth from Stereo Videos

DynamicStereo: Consistent Dynamic Depth from Stereo Videos

引用

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Karaev, Nikita Rocco, Ignacio Graham, Benjamin Neverova, Natalia Vedaldi, Andrea Rupprecht, Christian Univ Oxford Meta AI Oxford England Univ Oxford Visual Geometry Grp Oxford England

ISBN: (纸本)9798350301298

We consider the problem of reconstructing a dynamic scene observed from a stereo camera. Most existing methods for depth from stereo treat different stereo frames independently, leading to temporally inconsistent depth predictions. Temporal consistency is especially important for immersive AR or VR scenarios, where flickering greatly diminishes the user experience. We propose dynamicStereo, a novel transformer-based architecture to estimate disparity for stereo videos. The network learns to pool information from neighboring frames to improve the temporal consistency of its predictions. Our architecture is designed to process stereo videos efficiently through divided attention layers. We also introduce dynamic Replica, a new benchmark dataset containing synthetic videos of people and animals in scanned environments, which provides complementary training and evaluation data for dynamic stereo closer to real applications than existing datasets. Training with this dataset further improves the quality of predictions of our proposed dynamicStereo as well as prior methods. Finally, it acts as a benchmark for consistent stereo methods. Project page: https://***/

关键词： 3d from multi-view and sensors

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Meta Architecture for Point Cloud Analysis

Meta Architecture for Point Cloud Analysis

引用

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Lin, Haojia Zheng, Xiawu Li, Lijiang Chao, Fei Wang, Shanshan Wang, Yan Tian, Yonghong Ji, Rongrong Xiamen Univ Sch Informat Key Lab Multimedia Trusted Percept & Efficient Co Minist Educ China Xiamen 361005 Peoples R China Peng Cheng Lab Shenzhen Peoples R China Chinese Acad Sci Beijing Peoples R China Samsara Inc Washington DC USA Peking Univ Natl Engn Res Ctr Visual Technol Beijing Peoples R China Xiamen Univ Shenzhen Res Inst Xiamen Peoples R China

ISBN: (纸本)9798350301298

Recent advances in 3d point cloud analysis bring a diverse set of network architectures to the field. However, the lack of a unified framework to interpret those networks makes any systematic comparison, contrast, or analysis challenging, and practically limits healthy development of the field. In this paper, we take the initiative to explore and propose a unified framework called PointMeta, to which the popular 3d point cloud analysis approaches could fit. This brings three benefits. First, it allows us to compare different approaches in a fair manner, and use quick experiments to verify any empirical observations or assumptions summarized from the comparison. Second, the big picture brought by PointMeta enables us to think across different components, and revisit common beliefs and key design decisions made by the popular approaches. Third, based on the learnings from the previous two analyses, by doing simple tweaks on the existing approaches, we are able to derive a basic building block, termed PointMetaBase. It shows very strong performance in efficiency and effectiveness through extensive experiments on challenging benchmarks, and thus verifies the necessity and benefits of high-level interpretation, contrast, and comparison like PointMeta. In particular, PointMetaBase surpasses the previous state-of-the-art method by 0.7%/1.4/%2.1% mIoU with only 2%/11%/13% of the computation cost on the S3dIS datasets. The code and models are available at https://***/linhaojia13/PointMetaBase.

关键词： 3d from multi-view and sensors

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SUdS: Scalable Urban dynamic Scenes

SUDS: Scalable Urban Dynamic Scenes

引用

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Turki, Haithem Zhang, Jason Y. Ferroni, Francesco Ramanan, deva Carnegie Mellon Univ Pittsburgh PA 15213 USA Argo AI Pittsburgh PA USA

ISBN: (纸本)9798350301298

We extend neural radiance fields (NeRFs) to dynamic large-scale urban scenes. Prior work tends to reconstruct single video clips of short durations (up to 10 seconds). Two reasons are that such methods (a) tend to scale linearly with the number of moving objects and input videos because a separate model is built for each and (b) tend to require supervision via 3d bounding boxes and panoptic labels, obtained manually or via category-specific models. As a step towards truly open-world reconstructions of dynamic cities, we introduce two key innovations: (a) we factorize the scene into three separate hash table data structures to efficiently encode static, dynamic, and far-field radiance fields, and (b) we make use of unlabeled target signals consisting of RGB images, sparse LidAR, off-the-shelf self-supervised 2d descriptors, and most importantly, 2d optical flow. Operationalizing such inputs via photometric, geometric, and feature-metric reconstruction losses enables SUdS to decompose dynamic scenes into the static background, individual objects, and their motions. When combined with our multi-branch table representation, such reconstructions can be scaled to tens of thousands of objects across 1.2 million frames from 1700 videos spanning geospatial footprints of hundreds of kilometers, (to our knowledge) the largest dynamic NeRF built to date. We present qualitative initial results on a variety of tasks enabled by our representations, including novel-view synthesis of dynamic urban scenes, unsupervised 3d instance segmentation, and unsupervised 3d cuboid detection. To compare to prior work, we also evaluate on KITTI and Virtual KITTI 2, surpassing state-of-the-art methods that rely on ground truth 3d bounding box annotations while being 10x quicker to train.

关键词： 3d from multi-view and sensors

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CAP: Robust Point Cloud Classification via Semantic and Structural Modeling

CAP: Robust Point Cloud Classification via Semantic and Stru...

引用

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： ding, daizong Jiang, Erling Huang, Yuanmin Zhang, Mi Li, Wenxuan Yang, Min Fudan Univ Sch Comp Sci Shanghai Peoples R China

ISBN: (纸本)9798350301298

Recently, deep neural networks have shown great success on 3d point cloud classification tasks, which simultaneously raises the concern of adversarial attacks that cause severe damage to real-world applications. Moreover, defending against adversarial examples in point cloud data is extremely difficult due to the emergence of various attack strategies. In this work, with the insight of the fact that the adversarial examples in this task still preserve the same semantic and structural information as the original input, we design a novel defense framework for improving the robustness of existing classification models, which consists of two main modules: the attention-based pooling and the dynamic contrastive learning. In addition, we also develop an algorithm to theoretically certify the robustness of the proposed framework. Extensive empirical results on two datasets and three classification models show the robustness of our approach against various attacks, e.g., the averaged attack success rate of PointNet decreases from 70.2% to 2.7% on the ModelNet40 dataset under 9 common attacks.

关键词： 3d from multi-view and sensors

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Visual-Tactile Sensing for In-Hand Object Reconstruction

Visual-Tactile Sensing for In-Hand Object Reconstruction

引用

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Xu, Wenqiang Yu, Zhenjun Xue, Han Ye, Ruolin Yao, Siqiong Lu, Cewu Shanghai Jiao Tong Univ Shanghai Peoples R China Shanghai Qi Zhi Inst Shanghai Peoples R China Cornell Univ Ithaca NY USA

ISBN: (纸本)9798350301298

Tactile sensing is one of the modalities humans rely on heavily to perceive the world. Working with vision, this modality refines local geometry structure, measures deformation at the contact area, and indicates the hand-object contact state. With the availability of open-source tactile sensors such as dIGIT, research on visual-tactile learning is becoming more accessible and reproducible. Leveraging this tactile sensor, we propose a novel visual-tactile in-hand object reconstruction framework VTacO, and extend it to VTacOH for hand-object reconstruction. Since our method can support both rigid and deformable object reconstruction, no existing benchmarks are proper for the goal. We propose a simulation environment, VT-Sim, which supports generating hand-object interaction for both rigid and deformable objects. With VT-Sim, we generate a large-scale training dataset and evaluate our method on it. Extensive experiments demonstrate that our proposed method can outperform the previous baseline methods qualitatively and quantitatively. Finally, we directly apply our model trained in simulation to various real-world test cases, which display qualitative results. Codes, models, simulation environment, and datasets are available at https://***/view/vtaco/.

关键词： 3d from multi-view and sensors

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Learning to Measure the Point Cloud Reconstruction Loss in a Representation Space

Learning to Measure the Point Cloud Reconstruction Loss in a...

引用

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Huang, Tianxin ding, Zhonggan Zhang, Jiangning Tai, Ying Zhang, Zhenyu Chen, Mingang Wang, Chengjie Liu, Yong Zhejiang Univ APRIL Lab Hangzhou Peoples R China Tencent YouTu Lab Shanghai Peoples R China Shanghai Dev Ctr Comp Software Technol Shanghai Peoples R China

ISBN: (纸本)9798350301298

For point cloud reconstruction-related tasks, the reconstruction losses to evaluate the shape differences between reconstructed results and the ground truths are typically used to train the task networks. Most existing works measure the training loss with point-to-point distance, which may introduce extra defects as predefined matching rules may deviate from the real shape differences. Although some learning-based works have been proposed to overcome the weaknesses of manually-defined rules, they still measure the shape differences in 3d Euclidean space, which may limit their ability to capture defects in reconstructed shapes. In this work, we propose a learning-based Contrastive Adversarial Loss (CALoss) to measure the point cloud reconstruction loss dynamically in a non-linear representation space by combining the contrastive constraint with the adversarial strategy. Specifically, we use the contrastive constraint to help CALoss learn a representation space with shape similarity, while we introduce the adversarial strategy to help CALoss mine differences between reconstructed results and ground truths. According to experiments on reconstruction-related tasks, CALoss can help task networks improve reconstruction performances and learn more representative representations.

关键词： 3d from multi-view and sensors

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Patch-based 3d Natural Scene Generation from a Single Example

Patch-based 3D Natural Scene Generation from a Single Exampl...

引用

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Li, Weiyu Chen, Xuelin Wang, Jue Chen, Baoquan Shandong Univ Jinan Peoples R China Tencent AI Lab Beijing Peoples R China Peking Univ Beijing Peoples R China

ISBN: (纸本)9798350301298

We target a 3d generative model for general natural scenes that are typically unique and intricate. Lacking the necessary volumes of training data, along with the difficulties of having ad hoc designs in presence of varying scene characteristics, renders existing setups intractable. Inspired by classical patch-based image models, we advocate for synthesizing 3d scenes at the patch level, given a single example. At the core of this work lies important algorithmic designs w.r.t the scene representation and generative patch nearest-neighbor module, that address unique challenges arising from lifting classical 2d patch-based framework to 3d generation. These design choices, on a collective level, contribute to a robust, effective, and efficient model that can generate high-quality general natural scenes with both realistic geometric structure and visual appearance, in large quantities and varieties, as demonstrated upon a variety of exemplar scenes. data and code can be found at http://***/Sin3dGen.

关键词： 3d from multi-view and sensors

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Painting 3d Nature in 2d: view Synthesis of Natural Scenes from a Single Semantic Mask

Painting 3D Nature in 2D: View Synthesis of Natural Scenes f...

引用

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zhang, Shangzhan Peng, Sida Chen, Tianrun Mou, Linzhan Lin, Haotong Yu, Kaicheng Liao, Yiyi Zhou, Xiaowei Zhejiang Univ Hangzhou Zhejiang Peoples R China Alibaba Grp Hangzhou Zhejiang Peoples R China Zhejiang Univ State Key Lab CAD&CG Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9798350301298

We introduce a novel approach that takes a single semantic mask as input to synthesize multi-view consistent color images of natural scenes, trained with a collection of single images from the Internet. Prior works on 3d-aware image synthesis either require multi-view supervision or learning category-level prior for specific classes of objects, which are inapplicable to natural scenes. Our key idea to solve this challenge is to use a semantic field as the intermediate representation, which is easier to reconstruct from an input semantic mask and then translated to a radiance field with the assistance of off-the-shelf semantic image synthesis models. Experiments show that our method outperforms baseline methods and produces photorealistic and multiview consistent videos of a variety of natural scenes. The project website is https://***/paintingnature/.

关键词： 3d from multi-view and sensors

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Implicit Surface Contrastive Clustering for LidAR Point Clouds

Implicit Surface Contrastive Clustering for LiDAR Point Clou...

引用

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zhang, Zaiwei Bai, Min Li, Li Erran Nuro Inc Mountain View CA 94043 USA AWS AI East Palo Alto CA USA

ISBN: (纸本)9798350301298

Self-supervised pretraining on large unlabeled datasets has shown tremendous success in improving the task performance of many 2d and small scale 3d computer vision tasks. However, the popular pretraining approaches have not been impactfully applied to outdoor LidAR point cloud perception due to the latter's scene complexity and wide range. We propose a new self-supervised pretraining method ISCC with two novel pretext tasks for LidAR point clouds. The first task uncovers semantic information by sorting local groups of points in the scene into a globally consistent set of semantically meaningful clusters using contrastive learning, complemented by a second task which reasons about precise surfaces of various parts of the scene through implicit surface reconstruction to learn geometric structures. We demonstrate their effectiveness through transfer learning on 3d object detection and semantic segmentation in real world LidAR scenes. We further design an unsupervised semantic grouping task to show that our approach learns highly semantically meaningful features.

关键词： 3d from multi-view and sensors

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GarmentTracking: Category-Level Garment Pose Tracking

GarmentTracking: Category-Level Garment Pose Tracking

引用

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Xue, Han Xu, Wenqiang Zhang, Jieyi Tang, Tutian Li, Yutong du, Wenxin Ye, Ruolin Lu, Cewu Shanghai Qi Zhi Inst Shanghai Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China Cornell Univ Ithaca NY USA Shanghai Jiao Tong Univ Qing Yuan Res Inst Shanghai Peoples R China Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China

ISBN: (纸本)9798350301298

Garments are important to humans. A visual system that can estimate and track the complete garment pose can be useful for many downstream tasks and real-world applications. In this work, we present a complete package to address the category-level garment pose tracking task: (1) A recording system VR-Garment, with which users can manipulate virtual garment models in simulation through a VR interface. (2) A large-scale dataset VR-Folding, with complex garment pose configurations in manipulation like flattening and folding. (3) An end-to-end online tracking framework GarmentTracking, which predicts complete garment pose both in canonical space and task space given a point cloud sequence. Extensive experiments demonstrate that the proposed GarmentTracking achieves great performance even when the garment has large non-rigid deformation. It outperforms the baseline approach on both speed and accuracy. We hope our proposed solution can serve as a platform for future research. Codes and datasets are available in https://***.

关键词： 3d from multi-view and sensors

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