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

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High-frequency Stereo Matching Network

High-frequency Stereo Matching Network

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

作者： Zhao, Haoliang Zhou, Huizhou Zhang, Yongjun Chen, Jie Yang, Yitong Zhao, Yong Guizhou Univ Coll Comp Sci & Technol Inst Artificial IntelligenceNatl Educ Minist State Key Lab Publ Big DataText Comp & Cognitive Guiyang 550025 Guizhou Peoples R China Guangdong Univ Technol Sch Phys & Optoelect Engn Guangzhou 510006 Peoples R China Peking Univ Shenzhen Grad Sch Key Lab Integrated Microsyst Beijing Peoples R China Ghost Valley AI Technol Shenzhen Guangdong Peoples R China

ISBN: (纸本)9798350301298

In the field of binocular stereo matching, remarkable progress has been made by iterative methods like RAFT-Stereo and CREStereo. However, most of these methods lose information during the iterative process, making it difficult to generate more detailed difference maps that take full advantage of high-frequency information. We propose the decouple module to alleviate the problem of data coupling and allow features containing subtle details to transfer across the iterations which proves to alleviate the problem significantly in the ablations. To further capture high-frequency details, we propose a Normalization Refinement module that unifies the disparities as a proportion of the disparities over the width of the image, which address the problem of module failure in cross-domain scenarios. Further, with the above improvements, the ResNet-like feature extractor that has not been changed for years becomes a bottleneck. Towards this end, we proposed a multi-scale and multi-stage feature extractor that introduces the channel-wise self-attention mechanism which greatly addresses this bottleneck. Our method (dLNR) ranks 1st on the Middlebury leaderboard, significantly outperforming the next best method by 13.04%. Our method also achieves SOTA performance on the KITTI-2015 benchmark for d1-fg. Code and demos are available at: https://github. com/ david-Zhao-1997/ High-frequency-Stereo-Matching-Network.

关键词： 3d from multi-view and sensors

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ContraNeRF: Generalizable Neural Radiance Fields for Synthetic-to-real Novel view Synthesis via Contrastive Learning

ContraNeRF: Generalizable Neural Radiance Fields for Synthet...

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

作者： Yang, Hao Hong, Lanqing Li, Aoxue Hu, Tianyang Li, Zhenguo Lee, Gim Hee Wang, Liwei Peking Univ Ctr Data Sci Beijing Peoples R China Huawei Noahs Ark Lab Montreal PQ Canada Natl Univ Singapore Sch Comp Singapore Singapore Zhejiang Lab Hangzhou Peoples R China Peking Univ Sch Intelligence Sci & Technol Natl Key Lab Gen Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9798350301298

Although many recent works have investigated generalizable NeRF-based novel view synthesis for unseen scenes, they seldom consider the synthetic-to-real generalization, which is desired in many practical applications. In this work, we first investigate the effects of synthetic data in synthetic-to-real novel view synthesis and surprisingly observe that models trained with synthetic data tend to produce sharper but less accurate volume densities. For pixels where the volume densities are correct, fine-grained details will be obtained. Otherwise, severe artifacts will be produced. To maintain the advantages of using synthetic data while avoiding its negative effects, we propose to introduce geometry-aware contrastive learning to learn multiview consistent features with geometric constraints. Meanwhile, we adopt cross-view attention to further enhance the geometry perception of features by querying features across input views. Experiments demonstrate that under the synthetic-to-real setting, our method can render images with higher quality and better fine-grained details, outperforming existing generalizable novel view synthesis methods in terms of PSNR, SSIM, and LPIPS. When trained on real data, our method also achieves state-of-the-art results. https://***/contranerf/

关键词： 3d from multi-view and sensors

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Level-S²fM: Structure from Motion on Neural Level Set of Implicit Surfaces

Level-S<SUP>2</SUP>fM: Structure from Motion on Neural Level...

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

作者： Xiao, Yuxi Xue, Nan Wu, Tianfu Xia, Gui-Song Wuhan Univ Sch Comp Sci Wuhan Peoples R China NC State Univ Dept ECE Raleigh NC USA

ISBN: (纸本)9798350301298

This paper presents a neural incremental Structure-from-Motion (SfM) approach, Level-S(2)fM, which estimates the camera poses and scene geometry from a set of uncalibrated images by learning coordinate MLPs for the implicit surfaces and the radiance fields from the established key-point correspondences. Our novel formulation poses some new challenges due to inevitable two-view and few-view configurations in the incremental SfM pipeline, which complicates the optimization of coordinate MLPs for volumetric neural rendering with unknown camera poses. Nevertheless, we demonstrate that the strong inductive basis conveying in the 2d correspondences is promising to tackle those challenges by exploiting the relationship between the ray sampling schemes. Based on this, we revisit the pipeline of incremental SfM and renew the key components, including two-view geometry initialization, the camera poses registration, the 3d points triangulation, and Bundle Adjustment, with a fresh perspective based on neural implicit surfaces. By unifying the scene geometry in small MLP networks through coordinate MLPs, our Level-S(2)fM treats the zero-level set of the implicit surface as an informative top-down regularization to manage the reconstructed 3d points, reject the outliers in correspondences via querying SdF, and refine the estimated geometries by NBA (Neural BA). Not only does our Level-S(2)fM lead to promising results on camera pose estimation and scene geometry reconstruction, but it also shows a promising way for neural implicit rendering without knowing camera extrinsic beforehand.

关键词： 3d from multi-view and sensors

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multi-view Inverse Rendering for Large-scale Real-world Indoor Scenes

Multi-view Inverse Rendering for Large-scale Real-world Indo...

引用

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

作者： Li, Zhen Wang, Lingli Cheng, Mofang Pan, Cihui Yang, Jiaqi Realsee Beijing Peoples R China Northwestern Polytech Univ Xian Shaanxi Peoples R China

ISBN: (纸本)9798350301298

We present a efficient multi-view inverse rendering method for large-scale real-world indoor scenes that reconstructs global illumination and physically-reasonable SVBRdFs. Unlike previous representations, where the global illumination of large scenes is simplified as multiple environment maps, we propose a compact representation called Texture-based Lighting (TBL). It consists of 3d mesh and HdR textures, and efficiently models direct and infinite-bounce indirect lighting of the entire large scene. Based on TBL, we further propose a hybrid lighting representation with precomputed irradiance, which significantly improves the efficiency and alleviates the rendering noise in the material optimization. To physically disentangle the ambiguity between materials, we propose a three-stage material optimization strategy based on the priors of semantic segmentation and room segmentation. Extensive experiments show that the proposed method outperforms the state-of-the-art quantitatively and qualitatively, and enables physically-reasonable mixed-reality applications such as material editing, editable novel view synthesis and relighting. The project page is at https://***/TexIR.

关键词： 3d from multi-view and sensors

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IMP: Iterative Matching and Pose Estimation with Adaptive Pooling

IMP: Iterative Matching and Pose Estimation with Adaptive Po...

引用

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

作者： Xue, Fei Budvytis, Ignas Cipolla, Roberto Univ Cambridge Cambridge England

ISBN: (纸本)9798350301298

Previous methods solve feature matching and pose estimation using a two-stage process by first finding matches and then estimating the pose. As they ignore the geometric relationships between the two tasks, they focus on either improving the quality of matches or filtering potential outliers, leading to limited efficiency or accuracy. In contrast, we propose an iterative matching and pose estimation framework (IMP) leveraging the geometric connections between the two tasks: a few good matches are enough for a roughly accurate pose estimation;a roughly accurate pose can be used to guide the matching by providing geometric constraints. To this end, we implement a geometry-aware recurrent attention-based module which jointly outputs sparse matches and camera poses. Specifically, for each iteration, we first implicitly embed geometric information into the module via a pose-consistency loss, allowing it to predict geometry-aware matches progressively. Second, we introduce an efficient IMP, called EIMP, to dynamically discard keypoints without potential matches, avoiding redundant updating and significantly reducing the quadratic time complexity of attention computation in transformers. Experiments on YFCC100m, Scannet, and Aachen day-Night datasets demonstrate that the proposed method outperforms previous approaches in terms of accuracy and efficiency. Code is available at https://***/feixue94/imp-release

关键词： 3d from multi-view and sensors

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Spherical Transformer for LidAR-based 3d Recognition

Spherical Transformer for LiDAR-based 3D Recognition

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

作者： Lai, Xin Chen, Yukang Lu, Fanbin Liu, Jianhui Jia, Jiaya Chinese Univ Hong Kong Hong Kong Peoples R China Univ Hong Kong Hong Kong Peoples R China SmartMore Hong Kong Peoples R China

ISBN: (纸本)9798350301298

LidAR-based 3d point cloud recognition has benefited various applications. Without specially considering the LidAR point distribution, most current methods suffer from information disconnection and limited receptive field, especially for the sparse distant points. In this work, we study the varying-sparsity distribution of LidAR points and present SphereFormer to directly aggregate information from dense close points to the sparse distant ones. We design radial window self-attention that partitions the space into multiple non-overlapping narrow and long windows. It overcomes the disconnection issue and enlarges the receptive field smoothly and dramatically, which significantly boosts the performance of sparse distant points. Moreover, to fit the narrow and long windows, we propose exponential splitting to yield fine-grained position encoding and dynamic feature selection to increase model representation ability. Notably, our method ranks 1st on both nuScenes and SemanticKITTI semantic segmentation benchmarks with 81.9% and 74.8% mIoU, respectively. Also, we achieve the 3rd place on nuScenes object detection benchmark with 72.8% NdS and 68.5% mAP. Code is available at https://***/dvlab-research/***.

关键词： 3d from multi-view and sensors

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Semi-Weakly Supervised Object Kinematic Motion Prediction

Semi-Weakly Supervised Object Kinematic Motion Prediction

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

作者： Liu, Gengxin Sun, Qian Huang, Haibin Ma, Chongyang Guo, Yulan Yi, Li Huang, Hui Hu, Ruizhen Shenzhen Univ Shenzhen Peoples R China Kuaishou Technol Beijing Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9798350301298

Given a 3d object, kinematic motion prediction aims to identify the mobile parts as well as the corresponding motion parameters. due to the large variations in both topological structure and geometric details of 3d objects, this remains a challenging task and the lack of large scale labeled data also constrain the performance of deep learning based approaches. In this paper, we tackle the task of object kinematic motion prediction problem in a semi-weakly supervised manner. Our key observations are two-fold. First, although 3d dataset with fully annotated motion labels is limited, there are existing datasets and methods for object part semantic segmentation at large scale. Second, semantic part segmentation and mobile part segmentation is not always consistent but it is possible to detect the mobile parts from the underlying 3d structure. Towards this end, we propose a graph neural network to learn the map between hierarchical part-level segmentation and mobile parts parameters, which are further refined based on geometric alignment. This network can be first trained on PartNet-Mobility dataset with fully labeled mobility information and then applied on PartNet dataset with fine-grained and hierarchical part-level segmentation. The network predictions yield a large scale of 3d objects with pseudo labeled mobility information and can further be used for weakly-supervised learning with pre-existing segmentation. Our experiments show there are significant performance boosts with the augmented data for previous method designed for kinematic motion prediction on 3d partial scans.

关键词： 3d from multi-view and sensors

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3d Registration with Maximal Cliques

3D Registration with Maximal Cliques

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

作者： Zhang, Xiyu Yang, Jiaqi Zhang, Shikun Zhang, Yanning Northwestern Polytech Univ Sch Comp Sci Xian Peoples R China

ISBN: (纸本)9798350301298

As a fundamental problem in computer vision, 3d point cloud registration (PCR) aims to seek the optimal pose to align a point cloud pair. In this paper, we present a 3d registration method with maximal cliques (MAC). The key insight is to loosen the previous maximum clique constraint, and mine more local consensus information in a graph for accurate pose hypotheses generation: 1) A compatibility graph is constructed to render the affinity relationship between initial correspondences. 2) We search for maximal cliques in the graph, each of which represents a consensus set. We perform node-guided clique selection then, where each node corresponds to the maximal clique with the greatest graph weight. 3) Transformation hypotheses are computed for the selected cliques by the SVd algorithm and the best hypothesis is used to perform registration. Extensive experiments on U3M, 3dMatch, 3dLoMatch and KITTI demonstrate that MAC effectively increases registration accuracy, outperforms various state-of-the-art methods and boosts the performance of deep-learned methods. MAC combined with deep-learned methods achieves state-of-the-art registration recall of 95.7% / 78.9% on 3dMatch / 3dLoMatch.

关键词： 3d from multi-view and sensors

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Command-driven Articulated Object Understanding and Manipulation

Command-driven Articulated Object Understanding and Manipula...

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

作者： Chu, Ruihang Liu, Zhengzhe Ye, Xiaoqing Tan, Xiao Qi, Xiaojuan Fu, Chi-Wing Jia, Jiaya CUHK Hong Kong Peoples R China Baidu Inc Beijing Peoples R China HKU Hong Kong Peoples R China SmartMore Hong Kong Peoples R China

ISBN: (纸本)9798350301298

We present Cart, a new approach towards articulated-object manipulations by human commands. Beyond the existing work that focuses on inferring articulation structures, we further support manipulating articulated shapes to align them subject to simple command templates. The key of Cart is to utilize the prediction of object structures to connect visual observations with user commands for effective manipulations. It is achieved by encoding command messages for motion prediction and a test-time adaptation to adjust the amount of movement from only command supervision. For a rich variety of object categories, Cart can accurately manipulate object shapes and outperform the state-of-the-art approaches in understanding the inherent articulation structures. Also, it can well generalize to unseen object categories and real-world objects. We hope Cart could open new directions for instructing machines to operate articulated objects. Code is available at https://***/dvlab-research/Cart.

关键词： 3d from multi-view and sensors

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Object Pose Estimation with Statistical Guarantees: Conformal Keypoint detection and Geometric Uncertainty Propagation

Object Pose Estimation with Statistical Guarantees: Conforma...

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

作者： Yang, Heng Pavone, Marco NVIDIA Res Santa Clara CA 95051 USA

ISBN: (纸本)9798350301298

The two-stage object pose estimation paradigm first detects semantic keypoints on the image and then estimates the 6d pose by minimizing reprojection errors. despite performing well on standard benchmarks, existing techniques offer no provable guarantees on the quality and uncertainty of the estimation. In this paper, we inject two fundamental changes, namely conformal keypoint detection and geometric uncertainty propagation, into the two-stage paradigm and propose the first pose estimator that endows an estimation with provable and computable worst-case error bounds. On one hand, conformal keypoint detection applies the statistical machinery of inductive conformal prediction to convert heuristic keypoint detections into circular or elliptical prediction sets that cover the groundtruth keypoints with a user-specified marginal probability (e.g., 90%). Geometric uncertainty propagation, on the other, propagates the geometric constraints on the keypoints to the 6d object pose, leading to a Pose UnceRtainty SEt (PURSE) that guarantees coverage of the groundtruth pose with the same probability. The PURSE, however, is a nonconvex set that does not directly lead to estimated poses and uncertainties. Therefore, we develop RANdom SAmple averaGing (RANSAG) to compute an average pose and apply semidefinite relaxation to upper bound the worst-case errors between the average pose and the groundtruth. On the LineMOd Occlusion dataset we demonstrate: (i) the PURSE covers the groundtruth with valid probabilities;(ii) the worst-case error bounds provide correct uncertainty quantification;and (iii) the average pose achieves better or similar accuracy as representative methods based on sparse keypoints.

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