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

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3d Line Mapping Revisited

3D Line Mapping Revisited

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

作者： Liu, Shaohui Yu, Yifan Pautrat, Remi Pollefeys, Marc Larsson, Viktor Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland Microsoft Zurich Switzerland Lund Univ Lund Sweden

ISBN: (纸本)9798350301298

In contrast to sparse keypoints, a handful of line segments can concisely encode the high-level scene layout, as they often delineate the main structural elements. In addition to offering strong geometric cues, they are also omnipresent in urban landscapes and indoor scenes. despite their apparent advantages, current line-based reconstruction methods are far behind their point-based counterparts. In this paper we aim to close the gap by introducing LIMAP, a library for 3d line mapping that robustly and efficiently creates 3d line maps from multi-view imagery. This is achieved through revisiting the degeneracy problem of line triangulation, carefully crafted scoring and track building, and exploiting structural priors such as line coincidence, parallelism, and orthogonality. Our code integrates seamlessly with existing point-based Structure-from-Motion methods and can leverage their 3d points to further improve the line reconstruction. Furthermore, as a byproduct, the method is able to recover 3d association graphs between lines and points / vanishing points (VPs). In thorough experiments, we show that LIMAP significantly outperforms existing approaches for 3d line mapping. Our robust 3d line maps also open up new research directions. We show two example applications: visual localization and bundle adjustment, where integrating lines alongside points yields the best results. Code is available at https://***/cvg/limap.

关键词： 3d from multi-view and sensors

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depth Estimation from Camera Image and mmWave Radar Point Cloud

Depth Estimation from Camera Image and mmWave Radar Point Cl...

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

作者： Singh, Akash deep Ba, Yunhao Sarker, Ankur Zhang, Howard Kadambi, Achuta Soatto, Stefano Srivastava, Mani Wong, Alex Univ Calif Los Angeles Los Angeles CA 90024 USA Yale Univ New Haven CT USA

ISBN: (纸本)9798350301298

We present a method for inferring dense depth from a camera image and a sparse noisy radar point cloud. We first describe the mechanics behind mmWave radar point cloud formation and the challenges that it poses, i.e. ambiguous elevation and noisy depth and azimuth components that yields incorrect positions when projected onto the image, and how existing works have overlooked these nuances in camera-radar fusion. Our approach is motivated by these mechanics, leading to the design of a network that maps each radar point to the possible surfaces that it may project onto in the image plane. Unlike existing works, we do not process the raw radar point cloud as an erroneous depth map, but query each raw point independently to associate it with likely pixels in the image - yielding a semi-dense radar depth map. To fuse radar depth with an image, we propose a gated fusion scheme that accounts for the confidence scores of the correspondence so that we selectively combine radar and camera embeddings to yield a dense depth map. We test our method on the NuScenes benchmark and show a 10.3% improvement in mean absolute error and a 9.1% improvement in root-mean-square error over the best method. Code: https://github. com/nesl/radar-camera- fusion-depth.

关键词： 3d from multi-view and sensors

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Transforming Radiance Field with Lipschitz Network for Photorealistic 3d Scene Stylization

Transforming Radiance Field with Lipschitz Network for Photo...

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

作者： Zhang, Zicheng Liu, Yinglu Han, Congying Pan, Yingwei Guo, Tiande Yao, Ting Univ Chinese Acad Sci Beijing Peoples R China JD AI Res Beijing Peoples R China HiDream Ai Inc Denver CO USA

ISBN: (纸本)9798350301298

Recent advances in 3d scene representation and novel view synthesis have witnessed the rise of Neural Radiance Fields (NeRFs). Nevertheless, it is not trivial to exploit NeRF for the photorealistic 3d scene stylization task, which aims to generate visually consistent and photorealistic stylized scenes from novel views. Simply coupling NeRF with photorealistic style transfer (PST) will result in cross-view inconsistency and degradation of stylized view syntheses. Through a thorough analysis, we demonstrate that this non-trivial task can be simplified in a new light: When transforming the appearance representation of a pre-trained NeRF with Lipschitz mapping, the consistency and photorealism across source views will be seamlessly encoded into the syntheses. That motivates us to build a concise and flexible learning framework namely LipRF, which upgrades arbitrary 2d PST methods with Lipschitz mapping tailored for the 3d scene. Technically, LipRF first pre-trains a radiance field to reconstruct the 3d scene, and then emulates the style on each view by 2d PST as the prior to learn a Lipschitz network to stylize the pre-trained appearance. In view of that Lipschitz condition highly impacts the expressivity of the neural network, we devise an adaptive regularization to balance the reconstruction and stylization. A gradual gradient aggregation strategy is further introduced to optimize LipRF in a cost-efficient manner. We conduct extensive experiments to show the high quality and robust performance of LipRF on both photorealistic 3d stylization and object appearance editing.

关键词： 3d from multi-view and sensors

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OmniVidar: Omnidirectional depth Estimation from multi-Fisheye Images

OmniVidar: Omnidirectional Depth Estimation from Multi-Fishe...

引用

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

作者： Xie, Sheng Wang, daochuan Liu, Yunhui Harbin Inst Technol Harbin Peoples R China Hong Kong Ctr Logist Robot Hong Kong Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Estimating depth from four large field of view (FoV) cameras has been a difficult and understudied problem. In this paper, we proposed a novel and simple system that can convert this difficult problem into easier binocular depth estimation. We name this system OmniVidar, as its results are similar to LidAR, but rely only on vision. OmniVidar contains three components: (1) a new camera model to address the shortcomings of existing models, (2) a new multi-fisheye camera based epipolar rectification method for solving the image distortion and simplifying the depth estimation problem, (3) an improved binocular depth estimation network, which achieves a better balance between accuracy and efficiency. Unlike other omnidirectional stereo vision methods, OmniVidar does not contain any 3d convolution, so it can achieve higher resolution depth estimation at fast speed. Results demonstrate that OmniVidar outperforms all other methods in terms of accuracy and performance.

关键词： 3d from multi-view and sensors

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MobileNeRF: Exploiting the Polygon Rasterization Pipeline for Efficient Neural Field Rendering on Mobile Architectures

MobileNeRF: Exploiting the Polygon Rasterization Pipeline fo...

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

作者： Chen, Zhiqin Funkhouser, Thomas Hedman, Peter Tagliasacchi, Andrea Google Res Mountain View CA 94043 USA Simon Fraser Univ Burnaby BC Canada Univ Toronto Toronto ON Canada

ISBN: (纸本)9798350301298

Neural Radiance Fields (NeRFs) have demonstrated amazing ability to synthesize images of 3d scenes from novel views. However, they rely upon specialized volumetric rendering algorithms based on ray marching that are mismatched to the capabilities of widely deployed graphics hardware. This paper introduces a new NeRF representation based on textured polygons that can synthesize novel images efficiently with standard rendering pipelines. The NeRF is represented as a set of polygons with textures representing binary opacities and feature vectors. Traditional rendering of the polygons with a z-buffer yields an image with features at every pixel, which are interpreted by a small, view-dependent MLP running in a fragment shader to produce a final pixel color. This approach enables NeRFs to be rendered with the traditional polygon rasterization pipeline, which provides massive pixel-level parallelism, achieving interactive frame rates on a wide range of compute platforms, including mobile phones. Project page: https://***

关键词： 3d from multi-view and sensors

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HelixSurf: A Robust and Efficient Neural Implicit Surface Learning of Indoor Scenes with Iterative Intertwined Regularization

HelixSurf: A Robust and Efficient Neural Implicit Surface Le...

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

作者： Liang, Zhihao Huang, Zhangjin ding, Changxing Jia, Kui South China Univ Technol Guangzhou Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Recovery of an underlying scene geometry from multi-view images stands as a long-time challenge in computer vision research. The recent promise leverages neural implicit surface learning and differentiable volume rendering, and achieves both the recovery of scene geometry and synthesis of novel views, where deep priors of neural models are used as an inductive smoothness bias. While promising for object-level surfaces, these methods suffer when coping with complex scene surfaces. In the meanwhile, traditional multi-view stereo can recover the geometry of scenes with rich textures, by globally optimizing the local, pixel-wise correspondences across multiple views. We are thus motivated to make use of the complementary benefits from the two strategies, and propose a method termed Helix-shaped neural implicit Surface learning or HelixSurf;HelixSurf uses the intermediate prediction from one strategy as the guidance to regularize the learning of the other one, and conducts such intertwined regularization iteratively during the learning process. We also propose an efficient scheme for differentiable volume rendering in HelixSurf. Experiments on surface reconstruction of indoor scenes show that our method compares favorably with existing methods and is orders of magnitude faster, even when some of existing methods are assisted with auxiliary training data. The source code is available at https://***/Gorilla-LabSCUT/HelixSurf.

关键词： 3d from multi-view and sensors

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Neuraldome: A Neural Modeling Pipeline on multi-view Human-Object Interactions

NeuralDome: A Neural Modeling Pipeline on Multi-View Human-O...

引用

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

作者： Zhang, Juze Luo, Haimin Yang, Hongdi Xu, Xinru Wu, Qianyang Shi, Ye Yu, Jingyi Xu, Lan Wang, Jingya ShanghaiTech Univ Shanghai Peoples R China Chinese Acad Sci Shanghai Adv Res Inst Shanghai Peoples R China Univ Chinese Acad Sci Shanghai Peoples R China Shanghai Engn Res Ctr Intelligent Vis & Imaging Shanghai Peoples R China LumiAni Technol Hemel Hempstead England

ISBN: (纸本)9798350301298

Humans constantly interact with objects in daily life tasks. Capturing such processes and subsequently conducting visual inferences from a fixed viewpoint suffers from occlusions, shape and texture ambiguities, motions, etc. To mitigate the problem, it is essential to build a training dataset that captures free-viewpoint interactions. We construct a dense multi-view dome to acquire a complex human object interaction dataset, named HOdome, that consists of similar to 71M frames on 10 subjects interacting with 23 objects. To process the HOdome dataset, we develop Neuraldome, a layer-wise neural processing pipeline tailored for multi-view video inputs to conduct accurate tracking, geometry reconstruction and free-view rendering, for both human subjects and objects. Extensive experiments on the HOdome dataset demonstrate the effectiveness of Neuraldome on a variety of inference, modeling, and rendering tasks. Both the dataset and the Neuraldome tools will be disseminated to the community for further development, which can be found at https://***/Neuraldome

关键词： 3d from multi-view and sensors

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dualRefine: Self-Supervised depth and Pose Estimation Through Iterative Epipolar Sampling and Refinement Toward Equilibrium

DualRefine: Self-Supervised Depth and Pose Estimation Throug...

引用

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

作者： Bangunharcana, Antyanta Magd, Ahmed Kim, Kyung-Soo Korea Adv Inst Sci & Technol KAIST Mech Syst & Control Lab Daejeon South Korea Korea Adv Inst Sci & Technol KAIST Vehicular Syst Design & Control Lab Daejeon South Korea

ISBN: (纸本)9798350301298

Self-supervised multi-frame depth estimation achieves high accuracy by computing matching costs of pixel correspondences between adjacent frames, injecting geometric information into the network. These pixel-correspondence candidates are computed based on the relative pose estimates between the frames. Accurate pose predictions are essential for precise matching cost computation as they influence the epipolar geometry. Furthermore, improved depth estimates can, in turn, be used to align pose estimates. Inspired by traditional structure-from-motion (SfM) principles, we propose the dualRefine model, which tightly couples depth and pose estimation through a feedback loop. Our novel update pipeline uses a deep equilibrium model framework to iteratively refine depth estimates and a hidden state of feature maps by computing local matching costs based on epipolar geometry. Importantly, we used the refined depth estimates and feature maps to compute pose updates at each step. This update in the pose estimates slowly alters the epipolar geometry during the refinement process. Experimental results on the KITTI dataset demonstrate competitive depth prediction and odometry prediction performance surpassing published self-supervised baselines

关键词： 3d from multi-view and sensors

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VoxelNeXt: Fully Sparse VoxelNet for 3d Object detection and Tracking

VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and...

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

作者： Chen, Yukang Liu, Jianhui Zhang, Xiangyu Qi, Xiaojuan Jia, Jiaya Chinese Univ Hong Kong Hong Kong Peoples R China Univ Hong Kong Hong Kong Peoples R China MEGVII Beijing Peoples R China SmartMore Hong Kong Peoples R China

ISBN: (纸本)9798350301298

3d object detectors usually rely on hand-crafted proxies, e.g., anchors or centers, and translate well-studied 2d frameworks to 3d. Thus, sparse voxel features need to be densified and processed by dense prediction heads, which inevitably costs extra computation. In this paper, we instead propose VoxelNext for fully sparse 3d object detection. Our core insight is to predict objects directly based on sparse voxel features, without relying on hand-crafted proxies. Our strong sparse convolutional network VoxelNeXt detects and tracks 3d objects through voxel features entirely. It is an elegant and efficient framework, with no need for sparse-to-dense conversion or NMS post-processing. Our method achieves a better speed-accuracy trade-off than other mainframe detectors on the nuScenes dataset. For the first time, we show that a fully sparse voxel-based representation works decently for LIdAR 3d object detection and tracking. Extensive experiments on nuScenes, Waymo, and Argoverse2 benchmarks validate the effectiveness of our approach. Without bells and whistles, our model outperforms all existing LIdAR methods on the nuScenes tracking test benchmark. Code and models are available at ***/dvlab-research/VoxelNeXt.

关键词： 3d from multi-view and sensors

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Point2Pix: Photo-Realistic Point Cloud Rendering via Neural Radiance Fields

Point2Pix: Photo-Realistic Point Cloud Rendering via Neural ...

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

作者： Ho, Tao Xu, Xiaogang Liu, Shu Jia, Jiaya Chinese Univ Hong Kong Hong Kong Peoples R China SmartMore Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Synthesizing photo-realistic images from a point cloud is challenging because of the sparsity of point cloud representation. Recent Neural Radiance Fields and extensions are proposed to synthesize realistic images from 2d input. In this paper, we present Point2Pix as a novel point renderer to link the 3d sparse point clouds with 2d dense image pixels. Taking advantage of the point cloud 3d prior and NeRF rendering pipeline, our method can synthesize high-quality images from colored point clouds, generally for novel indoor scenes. To improve the efficiency of ray sampling, we propose point-guided sampling, which focuses on valid samples. Also, we present Point Encoding to build multiscale Radiance Fields that provide discriminative 3d point features. Finally, we propose Fusion Encoding to efficiently synthesize high-quality images. Extensive experiments on the ScanNet and ArkitScenes datasets demonstrate the effectiveness and generalization.

关键词： 3d from multi-view and sensors

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