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

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Grad-PU: Arbitrary-Scale Point Cloud Upsampling via Gradient descent with Learned distance Functions

Grad-PU: Arbitrary-Scale Point Cloud Upsampling via Gradient...

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

作者： He, Yun Tang, danhang Zhang, Yinda Xue, Xiangyang Fu, Yanwei Fudan Univ Sch Comp Sci Shanghai Peoples R China Fudan Univ Sch Data Sci Shanghai Peoples R China Zhejiang Normal Univ Shanghai Key Lab Intelligent Informat Proc Jinhua Peoples R China Zhejiang Normal Univ Fudan ISTBI ZJNU Algorithm Ctr Brain Inspired Int Jinhua Peoples R China Google Mountain View CA USA

ISBN: (纸本)9798350301298

Most existing point cloud upsampling methods have roughly three steps: feature extraction, feature expansion and 3d coordinate prediction. However, they usually suffer from two critical issues: (1) fixed upsampling rate after one-time training, since the feature expansion unit is customized for each upsampling rate;(2) outliers or shrinkage artifact caused by the difficulty of precisely predicting 3d coordinates or residuals of upsampled points. To adress them, we propose a new framework for accurate point cloud upsampling that supports arbitrary upsampling rates. Our method first interpolates the low-res point cloud according to a given upsampling rate. And then refine the positions of the interpolated points with an iterative optimization process, guided by a trained model estimating the difference between the current point cloud and the high-res target. Extensive quantitative and qualitative results on benchmarks and downstream tasks demonstrate that our method achieves the state-of-the-art accuracy and efficiency.

关键词： 3d from multi-view and sensors

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HGNet: Learning Hierarchical Geometry from Points, Edges, and Surfaces

HGNet: Learning Hierarchical Geometry from Points, Edges, an...

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

作者： Yao, Ting Li, Yehao Pan, Yingwei Mei, Tao HiDream Ai Inc Beijing Peoples R China

ISBN: (纸本)9798350301298

Parsing an unstructured point set into constituent local geometry structures (e.g., edges or surfaces) would be helpful for understanding and representing point clouds. This motivates us to design a deep architecture to model the hierarchical geometry from points, edges, surfaces (triangles), to super-surfaces (adjacent surfaces) for the thorough analysis of point clouds. In this paper, we present a novel Hierarchical Geometry Network (HGNet) that integrates such hierarchical geometry structures from super-surfaces, surfaces, edges, to points in a top-down manner for learning point cloud representations. Technically, we first construct the edges between every two neighbor points. A point-level representation is learnt with edge-to-point aggregation, i.e., aggregating all connected edges into the anchor point. Next, as every two neighbor edges compose a surface, we obtain the edge-level representation of each anchor edge via surface-to-edge aggregation over all neighbor surfaces. Furthermore, the surface-level representation is achieved through super-surface-to-surface aggregation by transforming all super-surfaces into the anchor surface. A Transformer structure is finally devised to unify all the point-level, edge-level, and surface-level features into the holistic point cloud representations. Extensive experiments on four point cloud analysis datasets demonstrate the superiority of HGNet for 3d object classification and part/semantic segmentation tasks. More remarkably, HGNet achieves the overall accuracy of 89.2% on ScanObjectNN, improving PointNeXt-S by 1.5%.

关键词： 3d from multi-view and sensors

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Two-view Geometry Scoring Without Correspondences

Two-view Geometry Scoring Without Correspondences

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

作者： Barroso-Laguna, Axel Brachmann, Eric Prisacariu, Victor Adrian Brostow, Gabriel Turmukhambetov, daniyar Niantic San Francisco CA 94111 USA Univ Oxford Oxford England UCL London England

ISBN: (纸本)9798350301298

Camera pose estimation for two-view geometry traditionally relies on RANSAC. Normally, a multitude of image correspondences leads to a pool of proposed hypotheses, which are then scored to find a winning model. The inlier count is generally regarded as a reliable indicator of "consensus". We examine this scoring heuristic, and find that it favors disappointing models under certain circumstances. As a remedy, we propose the Fundamental Scoring Network (FSNet), which infers a score for a pair of overlapping images and any proposed fundamental matrix. It does not rely on sparse correspondences, but rather embodies a two-view geometry model through an epipolar attention mechanism that predicts the pose error of the two images. FSNet can be incorporated into traditional RANSAC loops. We evaluate FSNet on fundamental and essential matrix estimation on indoor and outdoor datasets, and establish that FSNet can successfully identify good poses for pairs of images with few or unreliable correspondences. Besides, we show that naively combining FSNet with MAGSAC++ scoring approach achieves state of the art results.

关键词： 3d from multi-view and sensors

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EventNeRF: Neural Radiance Fields from a Single Colour Event Camera

EventNeRF: Neural Radiance Fields from a Single Colour Event...

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

作者： Rudnev, Viktor Elgharib, Mohamed Theobalt, Christian Golyanik, Vladislav Max Planck Inst Informat SIC Saarbrucken Germany Saarland Univ SIC Saarbrucken Germany

ISBN: (纸本)9798350301298

Asynchronously operating event cameras find many applications due to their high dynamic range, vanishingly low motion blur, low latency and low data bandwidth. The field saw remarkable progress during the last few years, and existing event-based 3d reconstruction approaches recover sparse point clouds of the scene. However, such sparsity is a limiting factor in many cases, especially in computer vision and graphics, that has not been addressed satisfactorily so far. Accordingly, this paper proposes the first approach for 3d-consistent, dense and photorealistic novel view synthesis using just a single colour event stream as input. At its core is a neural radiance field trained entirely in a self-supervised manner from events while preserving the original resolution of the colour event channels. Next, our ray sampling strategy is tailored to events and allows for data-efficient training. At test, our method produces results in the RGB space at unprecedented quality. We evaluate our method qualitatively and numerically on several challenging synthetic and real scenes and show that it produces significantly denser and more visually appealing renderings than the existing methods. We also demonstrate robustness in challenging scenarios with fast motion and under low lighting conditions. We release the newly recorded dataset and our source code to facilitate the research field, see https://***/EventNeRF.

关键词： 3d from multi-view and sensors

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Semi-Supervised Stereo-based 3d Object detection via Cross-view Consensus

Semi-Supervised Stereo-based 3D Object Detection via Cross-V...

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

作者： Wu, Wenhao Wong, Hau-San Wu, Si City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou Peoples R China

ISBN: (纸本)9798350301298

Stereo-based 3d object detection, which aims at detecting 3d objects with stereo cameras, shows great potential in low-cost deployment compared to LidAR-based methods and excellent performance compared to monocular-based algorithms. However, the impressive performance of stereo-based 3d object detection is at the huge cost of high-quality manual annotations, which are hardly attainable for any given scene. Semi-supervised learning, in which limited annotated data and numerous unannotated data are required to achieve a satisfactory model, is a promising method to address the problem of data deficiency. In this work, we propose to achieve semi-supervised learning for stereo-based 3d object detection through pseudo annotation generation from a temporal-aggregated teacher model, which temporally accumulates knowledge from a student model. To facilitate a more stable and accurate depth estimation, we introduce Temporal-Aggregation-Guided (TAG) disparity consistency, a cross-view disparity consistency constraint between the teacher model and the student model for robust and improved depth estimation. To mitigate noise in pseudo annotation generation, we propose a cross-view agreement strategy, in which pseudo annotations should attain high degree of agreements between 3d and 2d views, as well as between binocular views. We perform extensive experiments on the KITTI 3d dataset to demonstrate our proposed method's capability in leveraging a huge amount of unannotated stereo images to attain significantly improved detection results.

关键词： 3d from multi-view and sensors

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Long-term Visual Localization with Mobile sensors

Long-term Visual Localization with Mobile Sensors

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

作者： Yan, Shen Liu, Yu Wang, Long Shen, Zehong Peng, Zhen Liu, Haomin Zhang, Maojun Zhang, Guofeng Zhou, Xiaowei Natl Univ Def Technol Changsha Peoples R China SenseTime Res Hong Kong Peoples R China Zhejiang Univ Hangzhou Peoples R China

ISBN: (纸本)9798350301298

despite the remarkable advances in image matching and pose estimation, image-based localization of a camera in a temporally-varying outdoor environment is still a challenging problem due to huge appearance disparity between query and reference images caused by illumination, seasonal and structural changes. In this work, we propose to leverage additional sensors on a mobile phone, mainly GPS, compass, and gravity sensor, to solve this challenging problem. We show that these mobile sensors provide decent initial poses and effective constraints to reduce the searching space in image matching and final pose estimation. With the initial pose, we are also able to devise a direct 2d-3d matching network to efficiently establish 2d-3d correspondences instead of tedious 2d-2d matching in existing systems. As no public dataset exists for the studied problem, we collect a new dataset that provides a variety of mobile sensor data and significant scene appearance variations, and develop a system to acquire ground-truth poses for query images. We benchmark our method as well as several state-of-the-art baselines and demonstrate the effectiveness of the proposed approach. Our code and dataset are available on the project page: https://***/sensloc/.

关键词： 3d from multi-view and sensors

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PermutoSdF: Fast multi-view Reconstruction with Implicit Surfaces using Permutohedral Lattices

PermutoSDF: Fast Multi-View Reconstruction with Implicit Sur...

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

作者： Rosu, Radu Alexandru Behnke, Sven Univ Bonn Bonn Germany

ISBN: (纸本)9798350301298

Neural radiance-density field methods have become increasingly popular for the task of novel-view rendering. Their recent extension to hash-based positional encoding ensures fast training and inference with visually pleasing results. However, density-based methods struggle with recovering accurate surface geometry. Hybrid methods alleviate this issue by optimizing the density based on an underlying SdF. However, current SdF methods are overly smooth and miss fine geometric details. In this work, we combine the strengths of these two lines of work in a novel hash-based implicit surface representation. We propose improvements to the two areas by replacing the voxel hash encoding with a permutohedral lattice which optimizes faster, especially for higher dimensions. We additionally propose a regularization scheme which is crucial for recovering high-frequency geometric detail. We evaluate our method on multiple datasets and show that we can recover geometric detail at the level of pores and wrinkles while using only RGB images for supervision. Furthermore, using sphere tracing we can render novel views at 30 fps on an RTX 3090. Code is publicly available at https: //***/permuto_sdf

关键词： 3d from multi-view and sensors

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HexPlane: A Fast Representation for dynamic Scenes

HexPlane: A Fast Representation for Dynamic Scenes

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

作者： Cao, Ang Johnson, Justin Univ Michigan Ann Arbor MI 48109 USA

ISBN: (纸本)9798350301298

Modeling and re-rendering dynamic 3d scenes is a challenging task in 3d vision. Prior approaches build on NeRF and rely on implicit representations. This is slow since it requires many MLP evaluations, constraining real-world applications. We show that dynamic 3d scenes can be explicitly represented by six planes of learned features, leading to an elegant solution we call HexPlane. A HexPlane computes features for points in spacetime by fusing vectors extracted from each plane, which is highly efficient. Pairing a HexPlane with a tiny MLP to regress output colors and training via volume rendering gives impressive results for novel view synthesis on dynamic scenes, matching the image quality of prior work but reducing training time by more than 100x. Extensive ablations confirm our HexPlane design and show that it is robust to different feature fusion mechanisms, coordinate systems, and decoding mechanisms. HexPlane is a simple and effective solution for representing 4d volumes, and we hope they can broadly contribute to modeling spacetime for dynamic 3d scenes.

关键词： 3d from multi-view and sensors

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Controllable Mesh Generation Through Sparse Latent Point diffusion Models

Controllable Mesh Generation Through Sparse Latent Point Dif...

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

作者： Lyu, Zhaoyang Wang, Jinyi An, Yuwei Zhang, Ya Lin, dahua dai, Bo Shanghai AI Lab Shanghai Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9798350301298

Mesh generation is of great value in various applications involving computer graphics and virtual content, yet designing generative models for meshes is challenging due to their irregular data structure and inconsistent topology of meshes in the same category. In this work, we design a novel sparse latent point diffusion model for mesh generation. Our key insight is to regard point clouds as an intermediate representation of meshes, and model the distribution of point clouds instead. While meshes can be generated from point clouds via techniques like Shape as Points (SAP), the challenges of directly generating meshes can be effectively avoided. To boost the efficiency and controllability of our mesh generation method, we propose to further encode point clouds to a set of sparse latent points with pointwise semantic meaningful features, where two ddPMs are trained in the space of sparse latent points to respectively model the distribution of the latent point positions and features at these latent points. We find that sampling in this latent space is faster than directly sampling dense point clouds. Moreover, the sparse latent points also enable us to explicitly control both the overall structures and local details of the generated meshes. Extensive experiments are conducted on the ShapeNet dataset, where our proposed sparse latent point diffusion model achieves superior performance in terms of generation quality and controllability when compared to existing methods. Project page, code and appendix: https://***.

关键词： 3d from multi-view and sensors

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PLA: Language-driven Open-Vocabulary 3d Scene Understanding

PLA: Language-Driven Open-Vocabulary 3D Scene Understanding

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

作者： ding, Runyu Yang, Jihan Xue, Chuhui Zhang, Wenqing Bai, Song Qi, Xiaojuan Univ Hong Kong Hong Kong Peoples R China ByteDance Beijing Peoples R China

ISBN: (纸本)9798350301298

Open-vocabulary scene understanding aims to localize and recognize unseen categories beyond the annotated label space. The recent breakthrough of 2d open-vocabulary perception is largely driven by Internet-scale paired image-text data with rich vocabulary concepts. However, this success cannot be directly transferred to 3d scenarios due to the inaccessibility of large-scale 3d-text pairs. To this end, we propose to distill knowledge encoded in pre-trained vision-language (VL) foundation models through captioning multi-view images from 3d, which allows explicitly associating 3d and semantic-rich captions. Further, to foster coarse-to-fine visual-semantic representation learning from captions, we design hierarchical 3d-caption pairs, leveraging geometric constraints between 3d scenes and multi-view images. Finally, by employing contrastive learning, the model learns language-aware embeddings that connect 3d and text for open-vocabulary tasks. Our method not only remarkably outperforms baseline methods by 25.8% similar to 44.7% hIoU and 14.5% similar to 50.4% hAP(50) in open-vocabulary semantic and instance segmentation, but also shows robust transferability on challenging zero-shot domain transfer tasks. See the project website at https://***/projects/PLA.

关键词： 3d from multi-view and sensors

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