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

共 249 条记录，以下是241-250 订阅

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Rethinking the Approximation Error in 3d Surface Fitting for Point Cloud Normal Estimation

Rethinking the Approximation Error in 3D Surface Fitting for...

引用

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

作者： du, Hang Yan, Xuejun Wang, Jingjing Xie, di Pu, Shiliang Hikvis Res Inst Hangzhou Peoples R China

ISBN: (纸本)9798350301298

Most existing approaches for point cloud normal estimation aim to locally fit a geometric surface and calculate the normal from the fitted surface. Recently, learning-based methods have adopted a routine of predicting point-wise weights to solve the weighted least-squares surface fitting problem. despite achieving remarkable progress, these methods overlook the approximation error of the fitting problem, resulting in a less accurate fitted surface. In this paper, we first carry out in-depth analysis of the approximation error in the surface fitting problem. Then, in order to bridge the gap between estimated and precise surface normals, we present two basic design principles: 1) applies the Z-direction Transform to rotate local patches for a better surface fitting with a lower approximation error;2) models the error of the normal estimation as a learnable term. We implement these two principles using deep neural networks, and integrate them with the state-of-the-art (SOTA) normal estimation methods in a plug-and-play manner. Extensive experiments verify our approaches bring benefits to point cloud normal estimation and push the frontier of state-of-the-art performance on both synthetic and real-world datasets. The code is available at https://***/hikvision-research/3dVision.

关键词： 3d from multi-view and sensors

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NeRF-Supervised deep Stereo

NeRF-Supervised Deep Stereo

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

作者： Tosi, Fabio Tonioni, Alessio de Gregorio, daniele Poggi, Matteo Univ Bologna Bologna Italy Google Inc Zurich Switzerland Eyecan Ai Bologna Italy

ISBN: (纸本)9798350301298

We introduce a novel framework for training deep stereo networks effortlessly and without any ground-truth. By leveraging state-of-the-art neural rendering solutions, we generate stereo training data from image sequences collected with a single handheld camera. On top of them, a NeRF-supervised training procedure is carried out, from which we exploit rendered stereo triplets to compensate for occlusions and depth maps as proxy labels. This results in stereo networks capable of predicting sharp and detailed disparity maps. Experimental results show that models trained under this regime yield a 30-40% improvement over existing self-supervised methods on the challenging Middle-bury dataset, filling the gap to supervised models and, most times, outperforming them at zero-shot generalization.

关键词： 3d from multi-view and sensors

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depth Estimation from Indoor Panoramas with Neural Scene Representation

Depth Estimation from Indoor Panoramas with Neural Scene Rep...

引用

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

作者： Chang, Wenjie Zhang, Yueyi Xiong, Zhiwei Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9798350301298

depth estimation from indoor panoramas is challenging due to the equirectangular distortions of panoramas and inaccurate matching. In this paper, we propose a practical framework to improve the accuracy and efficiency of depth estimation from multi-view indoor panoramic images with the Neural Radiance Field technology. Specifically, we develop two networks to implicitly learn the Signed distance Function for depth measurements and the radiance field from panoramas. We also introduce a novel spherical position embedding scheme to achieve high accuracy. For better convergence, we propose an initialization method for the network weights based on the Manhattan World Assumption. Furthermore, we devise a geometric consistency loss, leveraging the surface normal, to further refine the depth estimation. The experimental results demonstrate that our proposed method outperforms state-of-the-art works by a large margin in both quantitative and qualitative evaluations. Our source code is available at https://***/WJ-Chang-42/IndoorPanodepth.

关键词： 3d from multi-view and sensors

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AutoRecon: Automated 3d Object discovery and Reconstruction

AutoRecon: Automated 3D Object Discovery and Reconstruction

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

作者： Wang, Yuang He, Xingyi Peng, Sida Lin, Haotong Bao, Hujun Zhou, Xiaowei Zhejiang Univ State Key Lab CAD&CG Hangzhou Peoples R China

ISBN: (纸本)9798350301298

A fully automated object reconstruction pipeline is crucial for digital content creation. While the area of 3d reconstruction has witnessed profound developments, the removal of background to obtain a clean object model still relies on different forms of manual labor, such as bounding box labeling, mask annotations, and mesh manipulations. In this paper, we propose a novel framework named AutoRecon for the automated discovery and reconstruction of an object from multi-view images. We demonstrate that foreground objects can be robustly located and segmented from SfM point clouds by leveraging self-supervised 2d vision transformer features. Then, we reconstruct decomposed neural scene representations with dense supervision provided by the decomposed point clouds, resulting in accurate object reconstruction and segmentation. Experiments on the dTU, BlendedMVS and CO3d-V2 datasets demonstrate the effectiveness and robustness of AutoRecon. The code and supplementary material are available on the project page: https://***/autorecon/.

关键词： 3d from multi-view and sensors

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HyperReel: High-Fidelity 6-doF Video with Ray-Conditioned Sampling

HyperReel: High-Fidelity 6-DoF Video with Ray-Conditioned Sa...

引用

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

作者： Attal, Benjamin Huang, Jia-Bin Richardt, Christian Zollhofer, Michael Kopf, Johannes O'Toole, Matthew Kim, Changil Carnegie Mellon Univ Pittsburgh PA 15213 USA Univ Maryland College Pk MD USA Real Labs Res Pittsburgh PA USA Meta Cambridge MA USA

ISBN: (纸本)9798350301298

Volumetric scene representations enable photorealistic view synthesis for static scenes and form the basis of several existing 6-doF video techniques. However, the volume rendering procedures that drive these representations necessitate careful trade-offs in terms of quality, rendering speed, and memory efficiency. In particular, existing methods fail to simultaneously achieve real-time performance, small memory footprint, and high-quality rendering for challenging real-world scenes. To address these issues, we present HyperReel - a novel 6-doF video representation. The two core components of HyperReel are: (1) a ray-conditioned sample prediction network that enables high-fidelity, high frame rate rendering at high resolutions and (2) a compact and memory-efficient dynamic volume representation. Our 6-doF video pipeline achieves the best performance compared to prior and contemporary approaches in terms of visual quality with small memory requirements, while also rendering at up to 18 frames-per-second at megapixel resolution without any custom CUdA code.

关键词： 3d from multi-view and sensors

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expOSE: Accurate Initialization-Free Projective Factorization using Exponential Regularization

expOSE: Accurate Initialization-Free Projective Factorizatio...

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

作者： Iglesias, Jose Pedro Nilsson, Amanda Olsson, Carl Chalmers Univ Technol Gothenburg Sweden Lund Univ Lund Sweden

ISBN: (纸本)9798350301298

Bundle adjustment is a key component in practically all available Structure from Motion systems. While it is crucial for achieving accurate reconstruction, convergence to the right solution hinges on good initialization. The recently introduced factorization-based pOSE methods formulate a surrogate for the bundle adjustment error without reliance on good initialization. In this paper, we show that pOSE has an undesirable penalization of large depths. To address this we propose expOSE which has an exponential regularization that is negligible for positive depths. To achieve efficient inference we use a quadratic approximation that allows an iterative solution with VarPro. Furthermore, we extend the method with radial distortion robustness by decomposing the Object Space Error into radial and tangential components. Experimental results confirm that the proposed method is robust to initialization and improves reconstruction quality compared to state-of-the-art methods even without bundle adjustment refinement.

关键词： 3d from multi-view and sensors

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VolRecon: Volume Rendering of Signed Ray distance Functions for Generalizable multi-view Reconstruction

VolRecon: Volume Rendering of Signed Ray Distance Functions ...

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

作者： Ren, Yufan Wang, Fangjinhua Zhang, Tong Pollefeys, Marc Süsstrunk, Sabine IVRL IC EPFL Lausanne Switzerland Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland

ISBN: (纸本)9798350301298

The success of the Neural Radiance Fields (NeRF) in novel view synthesis has inspired researchers to propose neural implicit scene reconstruction. However, most existing neural implicit reconstruction methods optimize perscene parameters and therefore lack generalizability to new scenes. We introduce VolRecon, a novel generalizable implicit reconstruction method with Signed Ray distance Function (SRdF). To reconstruct the scene with fine details and little noise, VolRecon combines projection features aggregated from multi-view features, and volume features interpolated from a coarse global feature volume. Using a ray transformer, we compute SRdF values of sampled points on a ray and then render color and depth. On dTU dataset, VolRecon outperforms SparseNeuS by about 30% in sparse view reconstruction and achieves comparable accuracy as MVSNet in full view reconstruction. Furthermore, our approach exhibits good generalization performance on the large-scale ETH3d benchmark. Code is available at https://***/IVRL/VolRecon/.

关键词： 3d from multi-view and sensors

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diffusion-SdF: Text-to-Shape via Voxelized diffusion

Diffusion-SDF: Text-to-Shape via Voxelized Diffusion

引用

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

作者： Li, Muheng duan, Yueqi Zhou, Jie Lu, Jiwen Tsinghua Univ Dept Automat Beijing Peoples R China Tsinghua Univ Dept Elect Engn Beijing Peoples R China

ISBN: (纸本)9798350301298

With the rising industrial attention to 3d virtual modeling technology, generating novel 3d content based on specified conditions (e.g. text) has become a hot issue. In this paper, we propose a new generative 3d modeling framework called diffusion-SdF for the challenging task of text-to-shape synthesis. Previous approaches lack flexibility in both 3d data representation and shape generation, thereby failing to generate highly diversified 3d shapes conforming to the given text descriptions. To address this, we propose a SdF autoencoder together with the Voxelized diffusion model to learn and generate representations for voxelized signed distance fields (SdFs) of 3d shapes. Specifically, we design a novel UinU-Net architecture that implants a local-focused inner network inside the standard U-Net architecture, which enables better reconstruction of patch-independent SdF representations. We extend our approach to further text-to-shape tasks including text-conditioned shape completion and manipulation. Experimental results show that diffusion-SdF generates both higher quality and more diversified 3d shapes that conform well to given text descriptions when compared to previous approaches. Code is available at: https: //***/ttlmh/diffusion-SdF.

关键词： 3d from multi-view and sensors

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A Rotation-Translation-decoupled Solution for Robust and Efficient Visual-Inertial Initialization

A Rotation-Translation-Decoupled Solution for Robust and Eff...

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

作者： He, Yijia Xu, Bo Ouyang, Zhanpeng Li, Hongdong Chinese Acad Sci Beijing Peoples R China Wuhan Univ Wuhan Peoples R China ShanghaiTech Univ Shanghai Peoples R China Australian Natl Univ Canberra ACT Australia

ISBN: (纸本)9798350301298

We propose a novel visual-inertial odometry (VIO) initialization method, which decouples rotation and translation estimation, and achieves higher efficiency and better robustness. Existing loosely-coupled VIO-initialization methods suffer from poor stability of visual structure-frommotion (SfM), whereas those tightly-coupled methods often ignore the gyroscope bias in the closed-form solution, resulting in limited accuracy. Moreover, the aforementioned two classes of methods are computationally expensive, because 3d point clouds need to be reconstructed simultaneously. In contrast, our new method fully combines inertial and visual measurements for both rotational and translational initialization. First, a rotation-only solution is designed for gyroscope bias estimation, which tightly couples the gyroscope and camera observations. Second, the initial velocity and gravity vector are solved with linear translation constraints in a globally optimal fashion and without reconstructing 3d point clouds. Extensive experiments have demonstrated that our method is 8 similar to 72 times faster (w.r.t. a 10-frame set) than the state-of-the-art methods, and also presents significantly higher robustness and accuracy.

关键词： 3d from multi-view and sensors

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