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

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Neural Kernel Surface Reconstruction

Neural Kernel Surface Reconstruction

引用

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

作者： Huang, Jiahui Gojcic, Zan Atzmon, Matan Litany, Or Fidler, Sanja Williams, Francis NVIDIA Santa Clara CA 78717 USA Univ Toronto Toronto ON Canada Vector Inst New York NY USA

ISBN: (纸本)9798350301298

We present a novel method for reconstructing a 3d implicit surface from a large-scale, sparse, and noisy point cloud. Our approach builds upon the recently introduced Neural Kernel Fields (NKF) [58] representation. It enjoys similar generalization capabilities to NKF, while simultaneously addressing its main limitations: (a) We can scale to large scenes through compactly supported kernel functions, which enable the use of memory-efficient sparse linear solvers. (b) We are robust to noise, through a gradient fitting solve. (c) We minimize training requirements, enabling us to learn from any dataset of dense oriented points, and even mix training data consisting of objects and scenes at different scales. Our method is capable of reconstructing millions of points in a few seconds, and handling very large scenes in an out-of-core fashion. We achieve state-of-the-art results on reconstruction benchmarks consisting of single objects (ShapeNet [5], ABC [33]), indoor scenes (ScanNet [11], Matterport3d [4]), and outdoor scenes (CARLA [16], Waymo [49]).

关键词： 3d from multi-view and sensors

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Unsupervised Inference of Signed distance Functions from Single Sparse Point Clouds without Learning Priors

Unsupervised Inference of Signed Distance Functions from Sin...

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

作者： Chen, Chao Liu, Yu-Shen Han, Zhizhong Tsinghua Univ Sch Software BNRist Beijing Peoples R China Wayne State Univ Dept Comp Sci Detroit MI USA

ISBN: (纸本)9798350301298

It is vital to infer signed distance functions (SdFs) from 3d point clouds. The latest methods rely on generalizing the priors learned from large scale supervision. However, the learned priors do not generalize well to various geometric variations that are unseen during training, especially for extremely sparse point clouds. To resolve this issue, we present a neural network to directly infer SdFs from single sparse point clouds without using signed distance supervision, learned priors or even normals. Our insight here is to learn surface parameterization and SdFs inference in an end-to-end manner. To make up the sparsity, we leverage parameterized surfaces as a coarse surface sampler to provide many coarse surface estimations in training iterations, according to which we mine supervision and our thin plate splines (TPS) based network infers SdFs as smooth functions in a statistical way. Our method significantly improves the generalization ability and accuracy in unseen point clouds. Our experimental results show our advantages over the state-of-the-art methods in surface reconstruction for sparse point clouds under synthetic datasets and real *** code is available at https://***/chenchao15/NeuralTPS.

关键词： 3d from multi-view and sensors

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Hi-LASSIE: High-Fidelity Articulated Shape and Skeleton discovery from Sparse Image Ensemble

Hi-LASSIE: High-Fidelity Articulated Shape and Skeleton Disc...

引用

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

作者： Yao, Chun-Han Hung, Wei-Chih Li, Yuanzhen Rubinstein, Michael Yang, Ming-Hsuan Jampani, Varun UC Merced Merced CA 95343 USA Waymo Mountain View CA USA Google Res Mountain View CA USA Yonsei Univ Seoul South Korea

ISBN: (纸本)9798350301298

Automatically estimating 3d skeleton, shape, camera viewpoints, and part articulation from sparse in-the-wild image ensembles is a severely under-constrained and challenging problem. Most prior methods rely on large-scale image datasets, dense temporal correspondence, or human annotations like camera pose, 2d keypoints, and shape templates. We propose Hi-LASSIE, which performs 3d articulated reconstruction from only 20-30 online images in the wild without any user-defined shape or skeleton templates. We follow the recent work of LASSIE that tackles a similar problem setting and make two significant advances. First, instead of relying on a manually annotated 3d skeleton, we automatically estimate a class-specific skeleton from the selected reference image. Second, we improve the shape reconstructions with novel instance-specific optimization strategies that allow reconstructions to faithful fit on each instance while preserving the class-specific priors learned across all images. Experiments on in-the-wild image ensembles show that Hi-LASSIE obtains higher fidelity state-of-the-art 3d reconstructions despite requiring minimum user input. Project page: chhankyao. ***/hi-lassie/

关键词： 3d from multi-view and sensors

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3d Semantic Segmentation in the Wild: Learning Generalized Models for Adverse-Condition Point Clouds

3D Semantic Segmentation in the Wild: Learning Generalized M...

引用

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

作者： Xiao, Aoran Huang, Jiaxing Xuan, Weihao Ren, Ruijie Liu, Kangcheng Guan, dayan El Saddik, Abdulmotaleb Lu, Shijian Xing, Eric Nanyang Technol Univ Singapore Singapore Waseda Univ Tokyo Japan Tech Univ Denmark Lyngby Denmark Mohamed bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates Carnegie Mellon Univ Pittsburgh PA USA Univ Ottawa Ottawa ON Canada

ISBN: (纸本)9798350301298

Robust point cloud parsing under all-weather conditions is crucial to level-5 autonomy in autonomous driving. However, how to learn a universal 3d semantic segmentation (3dSS) model is largely neglected as most existing benchmarks are dominated by point clouds captured under normal weather. We introduce SemanticSTF, an adverse-weather point cloud dataset that provides dense point-level annotations and allows to study 3dSS under various adverse weather conditions. We study all-weather 3dSS modeling under two setups: 1) domain adaptive 3dSS that adapts from normal-weather data to adverse-weather data;2) domain generalizable 3dSS that learns all-weather 3dSS models from normal-weather data. Our studies reveal the challenge while existing 3dSS methods encounter adverse-weather data, showing the great value of SemanticSTF in steering the future endeavor along this very meaningful research direction. In addition, we design a domain randomization technique that alternatively randomizes the geometry styles of point clouds and aggregates their embeddings, ultimately leading to a generalizable model that can improve 3dSS under various adverse weather effectively. The SemanticSTF and related codes are available at https: //***/xiaoaoran/SemanticSTF.

关键词： 3d from multi-view and sensors

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Starting from Non-Parametric Networks for 3d Point Cloud Analysis

Starting from Non-Parametric Networks for 3D Point Cloud Ana...

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

作者： Zhang, Renrui Wang, Liuhui Wang, Yali Gao, Peng Li, Hongsheng Shi, Jianbo CUHK MMLab Hong Kong Peoples R China Peking Univ Beijing Peoples R China Univ Penn Philadelphia PA 19104 USA Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China Heisenberg Robot Shenzhen Peoples R China

ISBN: (纸本)9798350301298

We present a Non-parametric Network for 3d point cloud analysis, Point-NN, which consists of purely non-learnable components: farthest point sampling (FPS), k-nearest neighbors (k-NN), and pooling operations, with trigonometric functions. Surprisingly, it performs well on various 3d tasks, requiring no parameters or training, and even surpasses existing fully trained models. Starting from this basic non-parametric model, we propose two extensions. First, Point-NN can serve as a base architectural framework to construct Parametric Networks by simply inserting linear layers on top. Given the superior non-parametric foundation, the derived Point-PN exhibits a high performance-efficiency trade-off with only a few learnable parameters. Second, Point-NN can be regarded as a plug-and-play module for the already trained 3d models during inference. Point-NN captures the complementary geometric knowledge and enhances existing methods for different 3d benchmarks without re-training. We hope our work may cast a light on the community for understanding 3d point clouds with non-parametric methods. Code is available at https:// github. com/ZrrSkywalker/Point-NN.

关键词： 3d from multi-view and sensors

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MACARONS: Mapping And Coverage Anticipation with RGB Online Self-Supervision

MACARONS: Mapping And Coverage Anticipation with RGB Online ...

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

作者： Guedon, Antoine Monnier, Tom Monasse, Pascal Lepetit, Vincent Univ Gustave Eiffel CNRS LIGM Ecole Ponts Champs Sur Marne France

ISBN: (纸本)9798350301298

We introduce a method that simultaneously learns to explore new large environments and to reconstruct them in 3d from color images only. This is closely related to the Next Best view problem (NBV), where one has to identify where to move the camera next to improve the coverage of an unknown scene. However, most of the current NBV methods rely on depth sensors, need 3d supervision and/or do not scale to large scenes. Our method requires only a color camera and no 3d supervision. It simultaneously learns in a self-supervised fashion to predict a "volume occupancy field" from color images and, from this field, to predict the NBV. Thanks to this approach, our method performs well on new scenes as it is not biased towards any training 3d data. We demonstrate this on a recent dataset made of various 3d scenes and show it performs even better than recent methods requiring a depth sensor, which is not a realistic assumption for outdoor scenes captured with a flying drone.

关键词： 3d from multi-view and sensors

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PersonNeRF : Personalized Reconstruction from Photo Collections

PersonNeRF : Personalized Reconstruction from Photo Collecti...

引用

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

作者： Weng, Chung-Yi Srinivasan, Pratul P. Curless, Brian Kemelmacher-Shlizerman, Ira Univ Washington Seattle WA 98195 USA Google Res Sunnyvale CA USA

ISBN: (纸本)9798350301298

We present PersonNeRF, a method that takes a collection of photos of a subject (e.g. Roger Federer) captured across multiple years with arbitrary body poses and appearances, and enables rendering the subject with arbitrary novel combinations of viewpoint, body pose, and appearance. PersonNeRF builds a customized neural volumetric 3d model of the subject that is able to render an entire space spanned by camera viewpoint, body pose, and appearance. A central challenge in this task is dealing with sparse observations;a given body pose is likely only observed by a single viewpoint with a single appearance, and a given appearance is only observed under a handful of different body poses. We address this issue by recovering a canonical T-pose neural volumetric representation of the subject that allows for changing appearance across different observations, but uses a shared pose-dependent motion field across all observations. We demonstrate that this approach, along with regularization of the recovered volumetric geometry to encourage smoothness, is able to recover a model that renders compelling images from novel combinations of viewpoint, pose, and appearance from these challenging unstructured photo collections, outperforming prior work for free-viewpoint human rendering.

关键词： 3d from multi-view and sensors

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K-Planes: Explicit Radiance Fields in Space, Time, and Appearance

K-Planes: Explicit Radiance Fields in Space, Time, and Appea...

引用

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

作者： Fridovich-Keil, Sara Meanti, Giacomo Warburg, Frederik Rahbaek Recht, Benjamin Kanazawa, Angjoo Univ Calif Berkeley Berkeley CA 94720 USA Ist Italiano Tecnol Genoa Italy Tech Univ Denmark Lyngby Denmark

ISBN: (纸本)9798350301298

We introduce k-planes, a white-box model for radiance fields in arbitrary dimensions. Our model uses ((d)(2)) ("d-choose-2") planes to represent a d-dimensional scene, providing a seamless way to go from static (d = 3) to dynamic (d = 4) scenes. This planar factorization makes adding dimension-specific priors easy, e.g. temporal smoothness and multi-resolution spatial structure, and induces a natural decomposition of static and dynamic components of a scene. We use a linear feature decoder with a learned color basis that yields similar performance as a nonlinear black-box MLP decoder. Across a range of synthetic and real, static and dynamic, fixed and varying appearance scenes, k-planes yields competitive and often state-of-the-art reconstruction fidelity with low memory usage, achieving 1000x compression over a full 4d grid, and fast optimization with a pure PyTorch implementation. For video results and code, please see ***/K-Planes.

关键词： 3d from multi-view and sensors

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Poly-PC: A Polyhedral Network for multiple Point Cloud Tasks at Once

Poly-PC: A Polyhedral Network for Multiple Point Cloud Tasks...

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

作者： Xie, Tao Wang, Shiguang Wang, Ke Yang, Linqi Jiang, Zhiqiang Zhang, Xingcheng dai, Kun Li, Ruifeng Cheng, Jian Harbin Inst Technol Harbin Peoples R China Univ Elect Sci & Technol China Chengdu Peoples R China SenseTime Res Beijing Peoples R China

ISBN: (纸本)9798350301298

In this work, we show that it is feasible to perform multiple tasks concurrently on point cloud with a straightforward yet effective multi-task network. Our framework, Poly-PC, tackles the inherent obstacles (e.g., different model architectures caused by task bias and conflicting gradients caused by multiple dataset domains, etc.) of multi-task learning on point cloud. Specifically, we propose a residual set abstraction (Res-SA) layer for efficient and effective scaling in both width and depth of the network, hence accommodating the needs of various tasks. We develop a weight-entanglement-based one-shot NAS technique to find optimal architectures for all tasks. Moreover, such technique entangles the weights of multiple tasks in each layer to offer task-shared parameters for efficient storage deployment while providing ancillary task-specific parameters for learning task-related features. Finally, to facilitate the training of Poly-PC, we introduce a task-prioritization-based gradient balance algorithm that leverages task prioritization to reconcile conflicting gradients, ensuring high performance for all tasks. Benefiting from the suggested techniques, models optimized by Poly-PC collectively for all tasks keep fewer total FLOPs and parameters and outperform previous methods. We also demonstrate that Poly-PC allows incremental learning and evades catastrophic forgetting when tuned to a new task.

关键词： 3d from multi-view and sensors

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Generating Part-Aware Editable 3d Shapes without 3d Supervision

Generating Part-Aware Editable 3D Shapes without 3D Supervis...

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

作者： Tertikas, Konstantinos Paschalidou, despoina Pan, Boxiao Park, Jeong Joon Uy, Mikaela Angelina Emiris, Ioannis Avrithis, Yannis Guibas, Leonidas Natl & Kapodistrian Univ Athens Athens Greece Stanford Univ Stanford CA USA Athena RC Maroussi Greece lnst Adv Res Artificial Intelligence IARAI Vienna Austria

ISBN: (纸本)9798350301298

Impressive progress in generative models and implicit representations gave rise to methods that can generate 3d shapes of high quality. However, being able to locally control and edit shapes is another essential property that can unlock several content creation applications. Local control can be achieved with part-aware models, but existing methods require 3d supervision and cannot produce textures. In this work, we devise PartNeRF, a novel part-aware generative model for editable 3d shape synthesis that does not require any explicit 3d supervision. Our model generates objects as a set of locally defined NeRFs, augmented with an affine transformation. This enables several editing operations such as applying transformations on parts, mixing parts from different objects etc. To ensure distinct, manipulable parts we enforce a hard assignment of rays to parts that makes sure that the color of each ray is only determined by a single NeRE As a result, altering one part does not affect the appearance of the others. Evaluations on various ShapeNet categories demonstrate the ability of our model to generate editable 3d objects of improvedfidelity, compared to previous part-based generative approaches that require 3d supervision or models relying on NeRFs.

关键词： 3d from multi-view and sensors

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