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

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Tensor4d: Efficient Neural 4d decomposition for High-fidelity dynamic Reconstruction and Rendering

Tensor4D: Efficient Neural 4D Decomposition for High-fidelit...

引用

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

作者： Shao, Ruizhi Zheng, Zerong Tu, Hanzhang Liu, Boning Zhang, Hongwen Liu, Yebin Tsinghua Univ Dept Automat Beijing Peoples R China NNKosmos Technol Norresundby Denmark

ISBN: (纸本)9798350301298

We present Tensor4d, an efficient yet effective approach to dynamic scene modeling. The key of our solution is an efficient 4d tensor decomposition method so that the dynamic scene can be directly represented as a 4d spatiotemporal tensor. To tackle the accompanying memory issue, we decompose the 4d tensor hierarchically by projecting it first into three time-aware volumes and then nine compact feature planes. In this way, spatial information over time can be simultaneously captured in a compact and memory-efficient manner. When applying Tensor4d for dynamic scene reconstruction and rendering, we further factorize the 4d fields to different scales in the sense that structural motions and dynamic detailed changes can be learned from coarse to fine. The effectiveness of our method is validated on both synthetic and real-world scenes. Extensive experiments show that our method is able to achieve high-quality dynamic reconstruction and rendering from sparse-view camera rigs or even a monocular camera. The code and dataset will be released at https://***/dSaurus/Tensor4d.

关键词： 3d from multi-view and sensors

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multiscale Tensor decomposition and Rendering Equation Encoding for view Synthesis

Multiscale Tensor Decomposition and Rendering Equation Encod...

引用

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

作者： Han, Kang Xiang, Wei James Cook Univ Douglas Qld Australia La Trobe Univ Melbourne Vic Australia

ISBN: (纸本)9798350301298

Rendering novel views from captured multi-view images has made considerable progress since the emergence of the neural radiance field. This paper aims to further advance the quality of view synthesis by proposing a novel approach dubbed the neural radiance feature field (NRFF). We first propose a multiscale tensor decomposition scheme to organize learnable features so as to represent scenes from coarse to fine scales. We demonstrate many benefits of the proposed multiscale representation, including more accurate scene shape and appearance reconstruction, and faster convergence compared with the single-scale representation. Instead of encoding view directions to model view-dependent effects, we further propose to encode the rendering equation in the feature space by employing the anisotropic spherical Gaussian mixture predicted from the proposed multiscale representation. The proposed NRFF improves state-of-the-art rendering results by over 1 dB in PSNR on both the NeRF and NSVF synthetic datasets. A significant improvement has also been observed on the realworld Tanks & Temples dataset. Code can be found at https://***/imkanghan/nrff.

关键词： 3d from multi-view and sensors

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PEAL: Prior-embedded Explicit Attention Learning for Low-overlap Point Cloud Registration

PEAL: Prior-embedded Explicit Attention Learning for Low-ove...

引用

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

作者： Yu, Junle Ren, Luwei Zhou, Wenhui Zhang, Yu Lin, Lili dai, Guojun Hangzhou Dianzi Univ Hangzhou Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China Zhejiang Gongshang Univ Hangzhou Peoples R China

ISBN: (纸本)9798350301298

Learning distinctive point-wise features is critical for low-overlap point cloud registration. Recently, it has achieved huge success in incorporating Transformer into point cloud feature representation, which usually adopts a self-attention module to learn intra-point-cloud features first, then utilizes a cross-attention module to perform feature exchange between input point clouds. The advantage of Transformer models mainly benefits from the use of self-attention to capture the global correlations in feature space. However, these global correlations may involve ambiguity for point cloud registration task, especially in indoor low-overlap scenarios, because the correlations with an extensive range of non-overlapping points may degrade the feature distinctiveness. To address this issue, we present PEAL, a Prior-embedded Explicit Attention Learning model. By incorporating prior knowledge into the learning process, the points are divided into two parts. One includes points lying in the putative overlapping region and the other includes points located in the putative non-overlapping region. Then PEAL explicitly learns one-way attention with the putative overlapping points. This simplistic design attains surprising performance, significantly relieving the aforementioned feature ambiguity. Our method improves the Registration Recall by 6+% on the challenging 3dLoMatch benchmark and achieves state-of-the-art performance on Feature Matching Recall, Inlier Ratio, and Registration Recall on both 3dMatch and 3dLoMatch.

关键词： 3d from multi-view and sensors

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BEV-Guided multi-Modality Fusion for driving Perception

BEV-Guided Multi-Modality Fusion for Driving Perception

引用

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

作者： Man, Yunze Gui, Liang-Yan Wang, Yu-Xiong UIUC Champaign IL USA

ISBN: (纸本)9798350301298

Integrating multiple sensors and addressing diverse tasks in an end-to-end algorithm are challenging yet critical topics for autonomous driving. To this end, we introduce BEVGuide, a novel Bird's Eye-view (BEV) representation learning framework, representing the first attempt to unify a wide range of sensors under direct BEV guidance in an end-to-end fashion. Our architecture accepts input from a diverse sensor pool, including but not limited to Camera, Lidar and Radar sensors, and extracts BEV feature embeddings using a versatile and general transformer backbone. We design a BEV-guided multi-sensor attention block to take queries from BEV embeddings and learn the BEV representation from sensor-specific features. BEVGuide is efficient due to its lightweight backbone design and highly flexible as it supports almost any input sensor configurations. Extensive experiments demonstrate that our framework achieves exceptional performance in BEV perception tasks with a diverse sensor set. Project page is at https://***/BEVGuide.

关键词： 3d from multi-view and sensors

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BAd-NeRF: Bundle Adjusted deblur Neural Radiance Fields

BAD-NeRF: Bundle Adjusted Deblur Neural Radiance Fields

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

作者： Wang, Peng Zhao, Lingzhe Ma, Ruijie Liu, Peidong Zhejiang Univ Hangzhou Peoples R China Westlake Univ Hangzhou Peoples R China

ISBN: (纸本)9798350301298

Neural Radiance Fields (NeRF) have received considerable attention recently, due to its impressive capability in photo-realistic 3d reconstruction and novel view synthesis, given a set of posed camera images. Earlier work usually assumes the input images are of good quality. However, image degradation (e.g. image motion blur in low-light conditions) can easily happen in real-world scenarios, which would further affect the rendering quality of NeRF. In this paper, we present a novel bundle adjusted deblur Neural Radiance Fields (BAd-NeRF), which can be robust to severe motion blurred images and inaccurate camera poses. Our approach models the physical image formation process of a motion blurred image, and jointly learns the parameters of NeRF and recovers the camera motion trajectories during exposure time. In experiments, we show that by directly modeling the real physical image formation process, BAd-NeRF achieves superior performance over prior works on both synthetic and real datasets. Code and data are available at https://***/WU-CVGL/BAd-NeRF.

关键词： 3d from multi-view and sensors

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VdN-NeRF: Resolving Shape-Radiance Ambiguity via view-dependence Normalization

VDN-NeRF: Resolving Shape-Radiance Ambiguity via View-Depend...

引用

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

作者： Zhu, Bingfan Yang, Yanchao Wang, Xulong Zheng, Youyi Guibas, Leonidas Zhejiang Univ Hangzhou Peoples R China Univ Hong Kong Hong Kong Peoples R China Stanford Univ Stanford CA USA

ISBN: (纸本)9798350301298

We propose VdN-NeRF, a method to train neural radiance fields (NeRFs) for better geometry under nonLambertian surface and dynamic lighting conditions that cause significant variation in the radiance of a point when viewed from different angles. Instead of explicitly modeling the underlying factors that result in the view-dependent phenomenon, which could be complex yet not inclusive, we develop a simple and effective technique that normalizes the view-dependence by distilling invariant information already encoded in the learned NeRFs. We then jointly train NeRFs for view synthesis with view-dependence normalization to attain quality geometry. Our experiments show that even though shape-radiance ambiguity is inevitable, the proposed normalization can minimize its effect on geometry, which essentially aligns the optimal capacity needed for explaining view-dependent variations. Our method applies to various baselines and significantly improves geometry without changing the volume rendering pipeline, even if the data is captured under a moving light source. Code is available at: https://***/BoifZ/VdN-NeRF.

关键词： 3d from multi-view and sensors

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Revisiting Rolling Shutter Bundle Adjustment: Toward Accurate and Fast Solution

Revisiting Rolling Shutter Bundle Adjustment: Toward Accurat...

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

作者： Liao, Bangyan Qu, delin Xue, Yifei Zhang, Huiqing Lao, Yizhen Hunan Univ Coll Comp Sci & Elect Engn Changsha Peoples R China Hunan Univ Coll Elect & Informat Engn Changsha Peoples R China Shanghai AI Lab Shanghai Peoples R China Jiangxi Prov Nat Resources Cause Dev Ctr Nanchang Peoples R China

ISBN: (纸本)9798350301298

We propose an accurate and fast bundle adjustment (BA) solution that estimates the 6-doF pose with an independent RS model of the camera and the geometry of the environment based on measurements from a rolling shutter (RS) camera. This tackles the challenges in the existing works, namely, relying on high frame rate video as input, restrictive assumptions on camera motion and poor efficiency. To this end, we first verify the positive influence of the image point normalization to RSBA. Then we present a novel visual residual covariance model to standardize the reprojection error during RSBA, which consequently improves the overall accuracy. Besides, we demonstrate the combination of Normalization and covariance standardization Weighting in RSBA (NW-RSBA) can avoid common planar degeneracy without the need to constrain the filming manner. Finally, we propose an acceleration strategy for NWRSBA based on the sparsity of its Jacobian matrix and Schur complement. The extensive synthetic and real data experiments verify the effectiveness and efficiency of the proposed solution over the state-of-the-art works.

关键词： 3d from multi-view and sensors

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GANmouflage: 3d Object Nondetection with Texture Fields

GANmouflage: 3D Object Nondetection with Texture Fields

引用

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

作者： Guo, Rui Collins, Jasmine de Lima, Oscar Owens, Andrew Univ Michigan Ann Arbor MI USA Univ Calif Berkeley Berkeley CA USA XMotors Ai Mountain View CA USA

ISBN: (纸本)9798350301298

We propose a method that learns to camouflage 3d objects within scenes. Given an object's shape and a distribution of viewpoints from which it will be seen, we estimate a texture that will make it difficult to detect. Successfully solving this task requires a model that can accurately reproduce textures from the scene, while simultaneously dealing with the highly conflicting constraints imposed by each viewpoint. We address these challenges with a model based on texture fields and adversarial learning. Our model learns to camouflage a variety of object shapes from randomly sampled locations and viewpoints within the input scene, and is the first to address the problem of hiding complex object shapes. Using a human visual search study, we find that our estimated textures conceal objects significantly better than previous methods.

关键词： 3d from multi-view and sensors

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Neural Kaleidoscopic Space Sculpting

Neural Kaleidoscopic Space Sculpting

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

作者： Ahn, Byeongjoo de Zeeuw, Michael Sankaranarayanan, Awin C. Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9798350301298

We introduce a method that recovers full-surround 3d reconstructions from a single kaleidoscopic image using a neural surface representation. Full-surround 3d reconstruction is critical for many applications, such as augmented and virtual reality. A kaleidoscope, which uses a single camera and multiple mirrors, is a convenient way of achieving full-surround coverage, as it redistributes light directions and thus captures multiple viewpoints in a single image. This enables single-shot and dynamic full-surround 3d reconstruction. However, using a kaleidoscopic image for multi-view stereo is challenging, as we need to decompose the image into multi-view images by identifying which pixel corresponds to which virtual camera, a process we call labeling. To address this challenge, pur approach avoids the need to explicitly estimate labels, but instead "sculpts" a neural surface representation through the careful use of silhouette, background, foreground, and texture information present in the kaleidoscopic image. We demonstrate the advantages of our method in a range of simulated and real experiments, on both static and dynamic scenes.

关键词： 3d from multi-view and sensors

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Lighteddepth: Video depth Estimation in light of Limited Inference view Angles

LightedDepth: Video Depth Estimation in light of Limited Inf...

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

作者： Zhu, Shengjie Liu, Xiaoming Michigan State Univ Dept Comp Sci & Engn E Lansing MI 48824 USA

ISBN: (纸本)9798350301298

Video depth estimation infers the dense scene depth from immediate neighboring video frames. While recent works consider it a simplified structure-from-motion (SfM) problem, it still differs from the SfM in that significantly fewer view angels are available in inference. This setting, however, suits the mono-depth and optical flow estimation. This observation motivates us to decouple the video depth estimation into two components, a normalized pose estimation over a flowmap and a logged residual depth estimation over a mono-depth map. The two parts are unified with an efficient off-the-shelf scale alignment algorithm. Additionally, we stabilize the indoor two-view pose estimation by including additional projection constraints and ensuring sufficient camera translation. Though a two-view algorithm, we validate the benefit of the decoupling with the substantial performance improvement over multi-view iterative prior works on indoor and outdoor datasets. Codes and models are available at https://***/ShngJZ/Lighteddepth.

关键词： 3d from multi-view and sensors

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