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检索条件"主题词=3D from Multi-view and Sensors"

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diffusion-Based Signed distance Fields for 3d Shape Generation

Diffusion-Based Signed Distance Fields for 3D Shape Generati...

引用

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

作者： Shim, Jaehyeok Kang, Changwoo Joo, Kyungdon UNIST Artificial Intelligence Grad Sch Ulsan South Korea

ISBN: (纸本)9798350301298

We propose a 3d shape generation framework (SdF-diffusion in short) that uses denoising diffusion models with continuous 3d representation via signed distance fields (SdF). Unlike most existing methods that depend on discontinuous forms, such as point clouds, SdF-diffusion generates high-resolution 3d shapes while alleviating memory issues by separating the generative process into two-stage: generation and super-resolution. In the first stage, a diffusion-based generative model generates a low-resolution SdF of 3d shapes. Using the estimated low-resolution SdF as a condition, the second stage diffusion model performs super-resolution to generate high-resolution SdF. Our framework can generate a high-fidelity 3d shape despite the extreme spatial complexity. On the ShapeNet dataset, our model shows competitive performance to the state-of-the-art methods and shows applicability on the shape completion task without modification.

关键词： 3d from multi-view and sensors

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Adaptive Patch deformation for Textureless-Resilient multi-view Stereo

Adaptive Patch Deformation for Textureless-Resilient Multi-V...

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

作者： Wang, Yuesong Zeng, Zhaojie Guan, Tao Yang, Wei Chen, Zhuo Liu, Wenkai Xu, Luoyuan Luo, Yawei Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan Peoples R China Zhejiang Univ Sch Comp Sci & Technol Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9798350301298

In recent years, deep learning-based approaches have shown great strength in multi-view stereo because of their outstanding ability to extract robust visual features. However, most learning-based methods need to build the cost volume and increase the receptive field enormously to get a satisfactory result when dealing with large-scale textureless regions, consequently leading to prohibitive memory consumption. To ensure both memory-friendly and textureless-resilient, we innovatively transplant the spirit of deformable convolution from deep learning into the traditional PatchMatch-based method. Specifically, for each pixel with matching ambiguity (termed unreliable pixel), we adaptively deform the patch centered on it to extend the receptive field until covering enough correlative reliable pixels (without matching ambiguity) that serve as anchors. When performing PatchMatch, constrained by the anchor pixels, the matching cost of an unreliable pixel is guaranteed to reach the global minimum at the correct depth and therefore increases the robustness of multi-view stereo significantly. To detect more anchor pixels to ensure better adaptive patch deformation, we propose to evaluate the matching ambiguity of a certain pixel by checking the convergence of the estimated depth as optimization proceeds. As a result, our method achieves state-of-the-art performance on ETH3d and Tanks and Temples while preserving low memory consumption.

关键词： 3d from multi-view and sensors

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Iterative Geometry Encoding Volume for Stereo Matching

Iterative Geometry Encoding Volume for Stereo Matching

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

作者： Xu, Gangwei Wang, Xianqi ding, Xiaohuan Yang, Xin Huazhong Univ Sci & Technol Sch EIC Wuhan Hubei Peoples R China

ISBN: (纸本)9798350301298

Recurrent All-Pairs Field Transforms (RAFT) has shown great potentials in matching tasks. However, all-pairs correlations lack non-local geometry knowledge and have difficulties tackling local ambiguities in ill-posed regions. In this paper, we propose Iterative Geometry Encoding Volume (IGEV-Stereo), a new deep network architecture for stereo matching. The proposed IGEV-Stereo builds a combined geometry encoding volume that encodes geometry and context information as well as local matching details, and iteratively indexes it to update the disparity map. To speed up the convergence, we exploit GEV to regress an accurate starting point for ConvGRUs iterations. Our IGEV-Stereo ranks 1(st) on KITTI 2015 and 2012 (Reflective) among all published methods and is the fastest among the top 10 methods. In addition, IGEV-Stereo has strong cross-dataset generalization as well as high inference efficiency. We also extend our IGEV to multi-view stereo (MVS), i.e. IGEV-MVS, which achieves competitive accuracy on dTU benchmark. Code is available at https://***/gangweiX/IGEV.

关键词： 3d from multi-view and sensors

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multi-view Stereo Representation Revist: Region-Aware MVSNet

Multi-View Stereo Representation Revist: Region-Aware MVSNet

引用

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

作者： Zhang, Yisu Zhu, Jianke Lin, Lixiang Zhejiang Univ Hangzhou Peoples R China Alibaba Zhejiang Univ Joint Res Inst Frontier Tec Hangzhou Peoples R China

ISBN: (纸本)9798350301298

deep learning-based multi-view stereo has emerged as a powerful paradigm for reconstructing the complete geometrically-detailed objects from multi-views. Most of the existing approaches only estimate the pixel-wise depth value by minimizing the gap between the predicted point and the intersection of ray and surface, which usually ignore the surface topology. It is essential to the textureless regions and surface boundary that cannot be properly reconstructed. To address this issue, we suggest to take advantage of point-to-surface distance so that the model is able to perceive a wider range of surfaces. To this end, we predict the distance volume from cost volume to estimate the signed distance of points around the surface. Our proposed RA-MVSNet is patch-awared, since the perception range is enhanced by associating hypothetical planes with a patch of surface. Therefore, it could increase the completion of textureless regions and reduce the outliers at the boundary. Moreover, the mesh topologies with fine details can be generated by the introduced distance volume. Comparing to the conventional deep learning-based multi-view stereo methods, our proposed RA-MVSNet approach obtains more complete reconstruction results by taking advantage of signed distance supervision. The experiments on both the dTU and Tanks & Temples datasets demonstrate that our proposed approach achieves the state-of-the-art results.

关键词： 3d from multi-view and sensors

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Teleidoscopic Imaging System for Microscale 3d Shape Reconstruction

Teleidoscopic Imaging System for Microscale 3D Shape Reconst...

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

作者： Kawahara, Ryo Kuo, Meng-Yu Jennifer Nobuhara, Shohei Kyushu Inst Technol Kitakyushu Fukuoka Japan Univ Minnesota Minneapolis MN 55455 USA Kyoto Univ Kyoto Japan

ISBN: (纸本)9798350301298

This paper proposes a practical method of microscale 3d shape capturing by a teleidoscopic imaging system. The main challenge in microscale 3d shape reconstruction is to capture the target from multiple viewpoints with a large enough depth-of-field. Our idea is to employ a teleidoscopic measurement system consisting of three planar mirrors and monocentric lens. The planar mirrors virtually define multiple viewpoints by multiple reflections, and the monocentric lens realizes a high magnification with less blurry and surround view even in closeup imaging. Our contributions include, a structured ray-pixel camera model which handles refractive and reflective projection rays efficiently, analytical evaluations of depth of field of our teleidoscopic imaging system, and a practical calibration algorithm of the teleidoscopic imaging system. Evaluations with real images prove the concept of our measurement system.

关键词： 3d from multi-view and sensors

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Virtual Occlusions Through Implicit depth

Virtual Occlusions Through Implicit Depth

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

作者： Watson, Jamie Sayed, Mohamed Qureshi, Zawar Brostow, Gabriel J. Vicente, Sara Mac Aodha, Oisin Firman, Michael Niantic London England Univ Edinburgh Edinburgh Scotland UCL London England

ISBN: (纸本)9798350301298

For augmented reality (AR), it is important that virtual assets appear to 'sit among' real world objects. The virtual element should variously occlude and be occluded by real matter, based on a plausible depth ordering. This occlusion should be consistent over time as the viewer's camera moves. Unfortunately, small mistakes in the estimated scene depth can ruin the downstream occlusion mask, and thereby the AR illusion. Especially in real-time settings, depths inferred near boundaries or across time can be inconsistent. In this paper, we challenge the need for depth-regression as an intermediate step. We instead propose an implicit model for depth and use that to predict the occlusion mask directly. The inputs to our network are one or more color images, plus the known depths of any virtual geometry. We show how our occlusion predictions are more accurate and more temporally stable than predictions derived from traditional depth-estimation models. We obtain state-of-the-art occlusion results on the challenging ScanNetv2 dataset and superior qualitative results on real scenes.

关键词： 3d from multi-view and sensors

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Privacy-Preserving Representations are not Enough: Recovering Scene Content from Camera Poses

Privacy-Preserving Representations are not Enough: Recoverin...

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

作者： Chelani, Kunal Sattler, Torsten Kahl, Fredrik Kukelova, Zuzana Chalmers Univ Technol Gothenburg Sweden Czech Tech Univ Czech Inst Informat Robot & Cybernet Prague Czech Republic Czech Tech Univ Visual Recognit Grp Fac Elect Engn Prague Czech Republic

ISBN: (纸本)9798350301298

Visual localization is the task of estimating the camera pose from which a given image was taken and is central to several 3d computer vision applications. With the rapid growth in the popularity of AR/VR/MR devices and cloud-based applications, privacy issues are becoming a very important aspect of the localization process. Existing work on privacy-preserving localization aims to defend against an attacker who has access to a cloud-based service. In this paper, we show that an attacker can learn about details of a scene without any access by simply querying a localization service. The attack is based on the observation that modern visual localization algorithms are robust to variations in appearance and geometry. While this is in general a desired property, it also leads to algorithms localizing objects that are similar enough to those present in a scene. An attacker can thus query a server with a large enough set of images of objects, e.g., obtained from the Internet, and some of them will be localized. The attacker can thus learn about object placements from the camera poses returned by the service (which is the minimal information returned by such a service). In this paper, we develop a proof-of-concept version of this attack and demonstrate its practical feasibility. The attack does not place any requirements on the localization algorithm used, and thus also applies to privacy-preserving representations. Current work on privacy-preserving representations alone is thus insufficient.

关键词： 3d from multi-view and sensors

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3d Video Loops from Asynchronous Input

3D Video Loops from Asynchronous Input

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

作者： Ma, Li Li, Xiaoyu Liao, Jing Sander, Pedro V. Hong Kong Univ Sci & Technol Hong Kong Peoples R China Tencent AI Lab Shenzhen Guangdong Peoples R China City Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Looping videos are short video clips that can be looped endlessly without visible seams or artifacts. They provide a very attractive way to capture the dynamism of natural scenes. Existing methods have been mostly limited to 2d representations. In this paper, we take a step forward and propose a practical solution that enables an immersive experience on dynamic 3d looping scenes. The key challenge is to consider the per-view looping conditions from asynchronous input while maintaining view consistency for the 3d representation. We propose a novel sparse 3d video representation, namely multi-Tile Video (MTV), which not only provides a view-consistent prior, but also greatly reduces memory usage, making the optimization of a 4d volume tractable. Then, we introduce a two-stage pipeline to construct the 3d looping MTV from completely asynchronous multi-view videos with no time overlap. A novel looping loss based on video temporal retargeting algorithms is adopted during the optimization to loop the 3d scene. Experiments of our framework have shown promise in successfully generating and rendering photorealistic 3d looping videos in real time even on mobile devices. The code, dataset, and live demos are available in https: //***/VideoLoop3d_web/.

关键词： 3d from multi-view and sensors

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dionysus: Recovering Scene Structures by dividing into Semantic Pieces

Dionysus: Recovering Scene Structures by Dividing into Seman...

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

作者： Wang, Likang Chen, Lei Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Guangzhou Guangdong Peoples R China Hong Kong Univ Sci & Technol Data Sci & Analyt Thrust Guangzhou Guangdong Peoples R China

ISBN: (纸本)9798350301298

Most existing 3d reconstruction methods result in either detail loss or unsatisfying efficiency. However, effectiveness and efficiency are equally crucial in real-world applications, e.g., autonomous driving and augmented reality. We argue that this dilemma comes from wasted resources on valueless depth samples. This paper tackles the problem by proposing a novel learning-based 3d reconstruction framework named dionysus. Our main contribution is to find out the most promising depth candidates from estimated semantic maps. This strategy simultaneously enables high effectiveness and efficiency by attending to the most reliable nominators. Specifically, we distinguish unreliable depth candidates by checking the cross-view semantic consistency and allow adaptive sampling by redistributing depth nominators among pixels. Experiments on the most popular datasets confirm our proposed framework's effectiveness.

关键词： 3d from multi-view and sensors

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Representing Volumetric Videos as dynamic MLP Maps

Representing Volumetric Videos as Dynamic MLP Maps

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

作者： Peng, Sida Yan, Yunzhi Shuai, Qing Bao, Hujun Zhou, Xiaowei Zhejiang Univ State Key Lab CAD&CG Hangzhou Peoples R China

ISBN: (纸本)9798350301298

This paper introduces a novel representation of volumetric videos for real-time view synthesis of dynamic scenes. Recent advances in neural scene representations demonstrate their remarkable capability to model and render complex static scenes, but extending them to represent dynamic scenes is not straightforward due to their slow rendering speed or high storage cost. To solve this problem, our key idea is to represent the radiance field of each frame as a set of shallow MLP networks whose parameters are stored in 2d grids, called MLP maps, and dynamically predicted by a 2d CNN decoder shared by all frames. Representing 3d scenes with shallow MLPs significantly improves the rendering speed, while dynamically predicting MLP parameters with a shared 2d CNN instead of explicitly storing them leads to low storage cost. Experiments show that the proposed approach achieves state-of-the-art rendering quality on the NHR and ZJU-MoCap datasets, while being efficient for real-time rendering with a speed of 41.7 fps for 512x512 images on an RTX 3090 GPU. The code is available at https://***/mlp_maps/.

关键词： 3d from multi-view and sensors

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