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

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NeuMap: Neural Coordinate Mapping by Auto-Transdecoder for Camera Localization

NeuMap: Neural Coordinate Mapping by Auto-Transdecoder for C...

引用

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

作者： Tang, Shitao Tang, Sicong Tagliasacchi, Andrea Tan, Ping Furukawa, Yasutaka Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9798350301298

This paper presents an end-to-end neural mapping method for camera localization, encoding a whole scene into a grid of latent codes, with which a Transformer-based auto-decoder regresses 3d coordinates of query pixels. State-of-the-art camera localization methods require each scene to be stored as a 3d point cloud with per-point features, which takes several gigabytes of storage per scene. While compression is possible, the performance drops significantly at high compression rates. NeuMap achieves extremely high compression rates with minimal performance drop by using 1) learnable latent codes to store scene information and 2) a scene-agnostic Transformer-based autodecoder to infer coordinates for a query pixel. The scene-agnostic network design also learns robust matching priors by training with large-scale data, and further allows us to just optimize the codes quickly for a new scene while fixing the network weights. Extensive evaluations with five benchmarks show that NeuMap outperforms all the other coordinate regression methods significantly and reaches similar performance as the feature matching methods while having a much smaller scene representation size. For example, NeuMap achieves 39.1% accuracy in Aachen night benchmark with only 6MB of data, while other compelling methods require 100MB or a few gigabytes and fail completely under high compression settings. The codes are available at https://***/Tangshitao/NeuMap.

关键词： 3d from multi-view and sensors

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PointConvFormer: Revenge of the Point-based Convolution

PointConvFormer: Revenge of the Point-based Convolution

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

作者： Wu, Wenxuan Li Fuxin Shan, Qi Oregon State Univ Corvallis OR 97331 USA CASIA Beijing Peoples R China Apple Inc Cupertino CA USA

ISBN: (纸本)9798350301298

We introduce PointConvFormer, a novel building block for point cloud based deep network architectures. Inspired by generalization theory, PointConvFormer combines ideas from point convolution, where filter weights are only based on relative position, and Transformers which utilize feature-based attention. In PointConvFormer, attention computed from feature difference between points in the neighborhood is used to modify the convolutional weights at each point. Hence, we preserved the invariances from point convolution, whereas attention helps to select relevant points in the neighborhood for convolution. PointConvFormer is suitable for multiple tasks that require details at the point level, such as segmentation and scene flow estimation tasks. We experiment on both tasks with multiple datasets including ScanNet, SemanticKitti, FlyingThings3d and KITTI. Our results show that PointConvFormer offers a better accuracy-speed tradeoff than classic convolutions, regular transformers, and voxelized sparse convolution approaches. Visualizations show that PointConvFormer performs similarly to convolution on flat areas, whereas the neighborhood selection effect is stronger on object boundaries, showing that it has got the best of both worlds. The code will be available.

关键词： 3d from multi-view and sensors

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Point Cloud Forecasting as a Proxy for 4d Occupancy Forecasting

Point Cloud Forecasting as a Proxy for 4D Occupancy Forecast...

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

作者： Khurana, Tarasha Hu, Peiyun Held, david Ramanan, deva Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9798350301298

Predicting how the world can evolve in the future is crucial for motion planning in autonomous systems. Classical methods are limited because they rely on costly human annotations in the form of semantic class labels, bounding boxes, and tracks or Hd maps of cities to plan their motion - and thus are difficult to scale to large unlabeled datasets. One promising self-supervised task is 3d point cloud forecasting [11, 18-20] from unannotated LidAR sequences. We show that this task requires algorithms to implicitly capture (1) sensor extrinsics (i.e., the egomotion of the autonomous vehicle), (2) sensor intrinsics (i.e., the sampling pattern specific to the particular LidAR sensor), and (3) the shape and motion of other objects in the scene. But autonomous systems should make predictions about the world and not their sensors! To this end, we factor out (1) and (2) by recasting the task as one of spacetime (4d) occupancy forecasting. But because it is expensive to obtain ground-truth 4d occupancy, we "render" point cloud data from 4d occupancy predictions given sensor extrinsics and intrinsics, allowing one to train and test occupancy algorithms with unannotated LidAR sequences. This also allows one to evaluate and compare point cloud forecasting algorithms across diverse datasets, sensors, and vehicles.

关键词： 3d from multi-view and sensors

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POEM: Reconstructing Hand in a Point Embedded multi-view Stereo

POEM: Reconstructing Hand in a Point Embedded Multi-view Ste...

引用

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

作者： Yang, Lixin Xu, Jian Zhong, Licheng Zhan, Xinyu Wang, Zhicheng Wu, Kejian Lu, Cewu Shanghai Jiao Tong Univ Shanghai Peoples R China Shanghai Qi Zhi Inst Shanghai Peoples R China Nreal Beijing Peoples R China

ISBN: (纸本)9798350301298

Enable neural networks to capture 3d geometrical-aware features is essential in multi-view based vision tasks. Previous methods usually encode the 3d information of multi-view stereo into the 2d features. In contrast, we present a novel method, named POEM, that directly operates on the 3d POints Embedded in the multi-view stereo for reconstructing hand mesh in it. Point is a natural form of 3d information and an ideal medium for fusing features across views, as it has different projections on different views. Our method is thus in light of a simple yet effective idea, that a complex 3d hand mesh can be represented by a set of 3d points that 1) are embedded in the multi-view stereo, 2) carry features from the multi-view images, and 3) encircle the hand. To leverage the power of points, we design two operations: point-based feature fusion and cross-set point attention mechanism. Evaluation on three challenging multi-view datasets shows that POEM outperforms the state-of-the-art in hand mesh reconstruction. Code and models are available for research at ***/lixiny/POEM

关键词： 3d from multi-view and sensors

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VL-SAT: Visual-Linguistic Semantics Assisted Training for 3d Semantic Scene Graph Prediction in Point Cloud

VL-SAT: Visual-Linguistic Semantics Assisted Training for 3D...

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

作者： Wang, Ziqin Cheng, Bowen Zhao, Lichen Xu, dong Tang, Yang Sheng, Lu Beihang Univ Sch Software Beijing Peoples R China Univ Hong Kong Hong Kong Peoples R China East China Univ Sci & Technol Shanghai Peoples R China

ISBN: (纸本)9798350301298

The task of 3d semantic scene graph (3dSSG) prediction in the point cloud is challenging since (1) the 3d point cloud only captures geometric structures with limited semantics compared to 2d images, and (2) long-tailed relation distribution inherently hinders the learning of unbiased prediction. Since 2d images provide rich semantics and scene graphs are in nature coped with languages, in this study, we propose Visual-Linguistic Semantics Assisted Training (VL-SAT) scheme that can significantly empower 3dSSG prediction models with discrimination about long-tailed and ambiguous semantic relations. The key idea is to train a powerful multi-modal oracle model to assist the 3d model. This oracle learns reliable structural representations based on semantics from vision, language, and 3d geometry, and its benefits can be heterogeneously passed to the 3d model during the training stage. By effectively utilizing visual-linguistic semantics in training, our VL-SAT can significantly boost common 3dSSG prediction models, such as SGFN and SGG(point), only with 3d inputs in the inference stage, especially when dealing with tail relation triplets. Comprehensive evaluations and ablation studies on the 3dSSG dataset have validated the effectiveness of the proposed scheme. Code is available at https://***/wz7in/CVPR2023-VLSAT.

关键词： 3d from multi-view and sensors

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OmniObject3d: Large-Vocabulary 3d Object dataset for Realistic Perception, Reconstruction and Generation

OmniObject3D: Large-Vocabulary 3D Object Dataset for Realist...

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

作者： Wu, Tong Zhang, Jiarui Fu, Xiao Wang, Yuxin Ren, Jiawei Pan, Liang Wu, Wayne Yang, Lei Wang, Jiaqi Qian, Chen Lin, dahua Liu, Ziwei Shanghai Artificial Intelligence Lab Shanghai Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China SenseTime Res Hong Kong Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China Nanyang Technol Univ S Lab Singapore Singapore

ISBN: (纸本)9798350301298

Recent advances in modeling 3d objects mostly rely on synthetic datasets due to the lack of large-scale real-scanned 3d databases. To facilitate the development of 3d perception, reconstruction, and generation in the real world, we propose OmniObject3d, a large vocabulary 3d object dataset with massive high-quality real-scanned 3d objects. OmniObject3d has several appealing properties: 1) Large Vocabulary: It comprises 6,000 scanned objects in 190 daily categories, sharing common classes with popular 2d datasets (e.g., ImageNet and LVIS), benefiting the pursuit of generalizable 3d representations. 2) Rich Annotations: Each 3d object is captured with both 2d and 3d sensors, providing textured meshes, point clouds, multiview rendered images, and multiple real-captured videos. 3) Realistic Scans: The professional scanners support highquality object scans with precise shapes and realistic appearances. With the vast exploration space offered by OmniObject3d, we carefully set up four evaluation tracks: a) robust 3d perception, b) novel-view synthesis, c) neural surface reconstruction, and d) 3d object generation. Extensive studies are performed on these four benchmarks, revealing new observations, challenges, and opportunities for future research in realistic 3d vision.

关键词： 3d from multi-view and sensors

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Learning the distribution of Errors in Stereo Matching for Joint disparity and Uncertainty Estimation

Learning the Distribution of Errors in Stereo Matching for J...

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

作者： Chen, Liyan Wang, Weihan Mordohai, Philippos Stevens Inst Technol Hoboken NJ 07030 USA

ISBN: (纸本)9798350301298

We present a new loss function for joint disparity and uncertainty estimation in deep stereo matching. Our work is motivated by the need for precise uncertainty estimates and the observation that multi-task learning often leads to improved performance in all tasks. We show that this can be achieved by requiring the distribution of uncertainty to match the distribution of disparity errors via a KL divergence term in the network's loss function. A differentiable soft-histogramming technique is used to approximate the distributions so that they can be used in the loss. We experimentally assess the effectiveness of our approach and observe significant improvements in both disparity and uncertainty prediction on large datasets. Our code is available at https://***/lly00412/***.

关键词： 3d from multi-view and sensors

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Learning 3d Scene Priors with 2d Supervision

Learning 3D Scene Priors with 2D Supervision

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

作者： Nie, Yinyu dai, Angela Han, Xiaoguang Niessner, Matthias Tech Univ Munich Munich Germany Chinese Univ Hong Kong Shenzhen Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Holistic 3d scene understanding entails estimation of both layout configuration and object geometry in a 3d environment. Recent works have shown advances in 3d scene estimation from various input modalities (e.g., images, 3d scans), by leveraging 3d supervision (e.g., 3d bounding boxes or CAd models), for which collection at scale is expensive and often intractable. To address this shortcoming, we propose a new method to learn 3d scene priors of layout and shape without requiring any 3d ground truth. Instead, we rely on 2d supervision from multi-view RGB images. Our method represents a 3d scene as a latent vector, from which we can progressively decode to a sequence of objects characterized by their class categories, 3d bounding boxes, and meshes. With our trained autoregressive decoder representing the scene prior, our method facilitates many downstream applications, including scene synthesis, interpolation, and single-view reconstruction. Experiments on 3d-FRONT and ScanNet show that our method outperforms state of the art in single-view reconstruction, and achieves state-of-the-art results in scene synthesis against baselines which require for 3d supervision.

关键词： 3d from multi-view and sensors

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Neural Vector Fields: Implicit Representation by Explicit Learning

Neural Vector Fields: Implicit Representation by Explicit Le...

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

作者： Yang, Xianghui Lin, Guosheng Chen, Zhenghao Zhou, Luping Univ Sydney Sydney NSW Australia Nanyang Technol Univ Singapore Singapore

ISBN: (纸本)9798350301298

deep neural networks (dNNs) are widely applied for nowadays 3d surface reconstruction tasks and such methods can be further divided into two categories, which respectively warp templates explicitly by moving vertices or represent 3d surfaces implicitly as signed or unsigned distance functions. Taking advantage of both advanced explicit learning process and powerful representation ability of implicit functions, we propose a novel 3d representation method, Neural Vector Fields (NVF). It not only adopts the explicit learning process to manipulate meshes directly, but also leverages the implicit representation of unsigned distance functions (UdFs) to break the barriers in resolution and topology. Specifically, our method first predicts the displacements from queries towards the surface and models the shapes as Vector Fields. Rather than relying on network differentiation to obtain direction fields as most existing UdF-based methods, the produced vector fields encode the distance and direction fields both and mitigate the ambiguity at "ridge" points, such that the calculation of direction fields is straightforward and differentiation-free. The differentiation-free characteristic enables us to further learn a shape codebook via Vector Quantization, which encodes the cross-object priors, accelerates the training procedure, and boosts model generalization on cross-category reconstruction. The extensive experiments on surface reconstruction benchmarks indicate that our method outperforms those state-of-the-art methods in different evaluation scenarios including watertight vs non-watertight shapes, category-specific vs category-agnostic reconstruction, category-unseen reconstruction, and cross-domain reconstruction. Our code is released at https: //***/Wi-sc/NVF.

关键词： 3d from multi-view and sensors

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BlendFields: Few-Shot Example-driven Facial Modeling

BlendFields: Few-Shot Example-Driven Facial Modeling

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

作者： Kania, Kacper Garbin, Stephan J. Tagliasacchi, Andrea Estellers, Virginia Yi, Kwang Moo Valentin, Julien Trzcinski, Tomasz Kowalski, Marek Warsaw Univ Technol Warsaw Poland Univ British Columbia Vancouver BC Canada Microsoft London England Simon Fraser Univ Burnaby BC Canada Google Brain Toronto ON Canada IDEAS NCBR Warsaw Poland Tooploox Wroclaw Poland

ISBN: (纸本)9798350301298

Generating faithful visualizations of human faces requires capturing both coarse and fine-level details of the face geometry and appearance. Existing methods are either data-driven, requiring an extensive corpus of data not publicly accessible to the research community, or fail to capture fine details because they rely on geometric face models that cannot represent fine-grained details in texture with a mesh discretization and linear deformation designed to model only a coarse face geometry. We introduce a method that bridges this gap by drawing inspiration from traditional computer graphics techniques. Unseen expressions are modeled by blending appearance from a sparse set of extreme poses. This blending is performed by measuring local volumetric changes in those expressions and locally reproducing their appearance whenever a similar expression is performed at test time. We show that our method generalizes to unseen expressions, adding fine-grained effects on top of smooth volumetric deformations of a face, and demonstrate how it generalizes beyond faces.

关键词： 3d from multi-view and sensors

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