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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31-40 订阅

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AnchorFormer: Point Cloud Completion from discriminative Nodes

AnchorFormer: Point Cloud Completion from Discriminative Nod...

引用

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

作者： Chen, Zhikai Long, Fuchen Qiu, Zhaofan Yao, Ting Zhou, Wengang Luo, Jiebo Mei, Tao Univ Sci & Technol China Hefei Peoples R China Univ Rochester Rochester NY USA HiDream ai Inc Beijing Peoples R China

ISBN: (纸本)9798350301298

Point cloud completion aims to recover the completed 3d shape of an object from its partial observation. A common strategy is to encode the observed points to a global feature vector and then predict the complete points through a generative process on this vector. Nevertheless, the results may suffer from the high-quality shape generation problem due to the fact that a global feature vector cannot sufficiently characterize diverse patterns in one object. In this paper, we present a new shape completion architecture, namely AnchorFormer, that innovatively leverages pattern-aware discriminative nodes, i.e., anchors, to dynamically capture regional information of objects. Technically, AnchorFormer models the regional discrimination by learning a set of anchors based on the point features of the input partial observation. Such anchors are scattered to both observed and unobserved locations through estimating particular offsets, and form sparse points together with the down-sampled points of the input observation. To reconstruct the fine-grained object patterns, AnchorFormer further employs a modulation scheme to morph a canonical 2d grid at individual locations of the sparse points into a detailed 3d structure. Extensive experiments on the PCN, ShapeNet-55/34 and KITTI datasets quantitatively and qualitatively demonstrate the efficacy of AnchorFormer over the state-of-the-art point cloud completion approaches. Source code is available at https://***/chenzhik/AnchorFormer.

关键词： 3d from multi-view and sensors

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multi-view Reconstruction using Signed Ray distance Functions (SRdF)

Multi-View Reconstruction using Signed Ray Distance Function...

引用

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

作者： Zins, Pierre Xu, Yuanlu Boyer, Edmond Wuhrer, Stefanie Tung, Tony Univ Grenoble Alpes Inria Ctr Grenoble France Meta Real Labs Sausalito CA 94025 USA Meta Real Labs Zurich Switzerland

ISBN: (纸本)9798350301298

In this paper, we investigate a new optimization framework for multi-view 3d shape reconstructions. Recent differentiable rendering approaches have provided breakthrough performances with implicit shape representations though they can still lack precision in the estimated geometries. On the other hand multi-view stereo methods can yield pixel wise geometric accuracy with local depth predictions along viewing rays. Our approach bridges the gap between the two strategies with a novel volumetric shape representation that is implicit but parameterized with pixel depths to better materialize the shape surface with consistent signed distances along viewing rays. The approach retains pixel-accuracy while benefiting from volumetric integration in the optimization. To this aim, depths are optimized by evaluating, at each 3d location within the volumetric discretization, the agreement between the depth prediction consistency and the photometric consistency for the corresponding pixels. The optimization is agnostic to the associated photo-consistency term which can vary from a median-based baseline to more elaborate criteria, e.g. learned functions. Our experiments demonstrate the benefit of the volumetric integration with depth predictions. They also show that our approach outperforms existing approaches over standard 3d benchmarks with better geometry estimations.

关键词： 3d from multi-view and sensors

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Revisiting Rotation Averaging: Uncertainties and Robust Losses

Revisiting Rotation Averaging: Uncertainties and Robust Loss...

引用

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

作者： Zhang, Ganlin Larsson, Viktor Barath, daniel Swiss Fed Inst Technol Zurich Switzerland Lund Univ Lund Sweden

ISBN: (纸本)9798350301298

In this paper, we revisit the rotation averaging problem applied in global Structure-from-Motion pipelines. We argue that the main problem of current methods is the minimized cost function that is only weakly connected with the input data via the estimated epipolar geometries. We propose to better model the underlying noise distributions by directly propagating the uncertainty from the point correspondences into the rotation averaging. Such uncertainties are obtained for free by considering the Jacobians of two-view refinements. Moreover, we explore integrating a variant of the MAGSAC loss into the rotation averaging problem, instead of using classical robust losses employed in current frameworks. The proposed method leads to results superior to baselines, in terms of accuracy, on large-scale public benchmarks. The code is public. https://***/zhangganlin/GlobalSfMpy

关键词： 3d from multi-view and sensors

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Building Rearticulable Models for Arbitrary 3d Objects from 4d Point Clouds

Building Rearticulable Models for Arbitrary 3D Objects from ...

引用

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

作者： Liu, Shaowei Gupta, Saurabh Wang, Shenlong Univ Illinois Champaign IL 61820 USA

ISBN: (纸本)9798350301298

We build rearticulable models for arbitrary everyday man-made objects containing an arbitrary number of parts that are connected together in arbitrary ways via 1 degree-of-freedom joints. Given point cloud videos of such everyday objects, our method identifies the distinct object parts, what parts are connected to what other parts, and the properties of the joints connecting each part pair. We do this by jointly optimizing the part segmentation, transformation, and kinematics using a novel energy minimization framework. Our inferred animatable models, enables retargeting to novel poses with sparse point correspondences guidance. We test our method on a new articulating robot dataset, and the Sapiens dataset with common daily objects. Experiments show that our method outperforms two leading prior works on various metrics.

关键词： 3d from multi-view and sensors

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PointVector: A Vector Representation In Point Cloud Analysis

PointVector: A Vector Representation In Point Cloud Analysis

引用

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

作者： deng, Xin Zhang, Wenyu ding, Qing Zhang, XinMing Univ Sci & Technol China Hefei Anhui Peoples R China

ISBN: (纸本)9798350301298

In point cloud analysis, point-based methods have rapidly developed in recent years. These methods have recently focused on concise MLP structures, such as PointNeXt, which have demonstrated competitiveness with Convolutional and Transformer structures. However, standard MLPs are limited in their ability to extract local features effectively. To address this limitation, we propose a Vector-oriented Point Set Abstraction that can aggregate neighboring features through higher-dimensional vectors. To facilitate network optimization, we construct a transformation from scalar to vector using independent angles based on 3d vector rotations. Finally, we develop a PointVector model that follows the structure of PointNeXt. Our experimental results demonstrate that PointVector achieves state-of-the-art performance 72.3% mIOU on the S3dIS Area 5 and 78.4% mIOU on the S3dIS (6-fold cross-validation) with only 58% model parameters of PointNeXt. We hope our work will help the exploration of concise and effective feature representations. The code will be released soon.

关键词： 3d from multi-view and sensors

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ProxyFormer: Proxy Alignment Assisted Point Cloud Completion with Missing Part Sensitive Transformer

ProxyFormer: Proxy Alignment Assisted Point Cloud Completion...

引用

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

作者： Li, Shanshan Gao, Pan Tan, Xiaoyang Wei, Mingqiang Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Peoples R China

ISBN: (纸本)9798350301298

Problems such as equipment defects or limited viewpoints will lead the captured point clouds to be incomplete. Therefore, recovering the complete point clouds from the partial ones plays an vital role in many practical tasks, and one of the keys lies in the prediction of the missing part. In this paper, we propose a novel point cloud completion approach namely ProxyFormer that divides point clouds into existing (input) and missing (to be predicted) parts and each part communicates information through its proxies. Specifically, we fuse information into point proxy via feature and position extractor, and generate features for missing point proxies from the features of existing point proxies. Then, in order to better perceive the position of missing points, we design a missing part sensitive transformer, which converts random normal distribution into reasonable position information, and uses proxy alignment to refine the missing proxies. It makes the predicted point proxies more sensitive to the features and positions of the missing part, and thus makes these proxies more suitable for subsequent coarse-to-fine processes. Experimental results show that our method outperforms state-of-the-art completion networks on several benchmark datasets and has the fastest inference speed.

关键词： 3d from multi-view and sensors

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multi-view Azimuth Stereo via Tangent Space Consistency

Multi-View Azimuth Stereo via Tangent Space Consistency

引用

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

作者： Cao, Xu Santo, Hiroaki Okura, Fumio Matsushita, Yasuyuki Osaka Univ Osaka Japan

ISBN: (纸本)9798350301298

We present a method for 3d reconstruction only using calibrated multi-view surface azimuth maps. Our method, multi-view azimuth stereo, is effective for textureless or specular surfaces, which are difficult for conventional multi-view stereo methods. We introduce the concept of tangent space consistency: multi-view azimuth observations of a surface point should be lifted to the same tangent space. Leveraging this consistency, we recover the shape by optimizing a neural implicit surface representation. Our method harnesses the robust azimuth estimation capabilities of photometric stereo methods or polarization imaging while bypassing potentially complex zenith angle estimation. Experiments using azimuth maps from various sources validate the accurate shape recovery with our method, even without zenith angles.

关键词： 3d from multi-view and sensors

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vMAP: Vectorised Object Mapping for Neural Field SLAM

vMAP: Vectorised Object Mapping for Neural Field SLAM

引用

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

作者： Kong, Xin Liu, Shikun Taher, Marwan davison, Andrew J. Imperial Coll London Dyson Robot Lab London England

ISBN: (纸本)9798350301298

We present vMAP, an object-level dense SLAM system using neural field representations. Each object is represented by a small MLP, enabling efficient, watertight object modelling without the need for 3d priors. As an RGB-d camera browses a scene with no prior information, vMAP detects object instances on-the-fly, and dynamically adds them to its map. Specifically, thanks to the power of vectorised training, vMAP can optimise as many as 50 individual objects in a single scene, with an extremely efficient training speed of 5Hz map update. We experimentally demonstrate significantly improved scene-level and object-level reconstruction quality compared to prior neural field SLAM systems. Project page: https: //***/vMAP.

关键词： 3d from multi-view and sensors

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Efficient Second-Order Plane Adjustment

Efficient Second-Order Plane Adjustment

引用

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

作者： Zhou, Lipu Meituan Beijing Peoples R China

ISBN: (纸本)9798350301298

Planes are generally used in 3d reconstruction for depth sensors, such as RGB-d cameras and LidARs. This paper focuses on the problem of estimating the optimal planes and sensor poses to minimize the point-to-plane distance. The resulting least-squares problem is referred to as plane adjustment (PA) in the literature, which is the counterpart of bundle adjustment (BA) in visual reconstruction. Iterative methods are adopted to solve these least-squares problems. Typically, Newton's method is rarely used for a large-scale least-squares problem, due to the high computational complexity of the Hessian matrix. Instead, methods using an approximation of the Hessian matrix, such as the Levenberg-Marquardt (LM) method, are generally adopted. This paper adopts the Newton's method to efficiently solve the PA problem. Specifically, given poses, the optimal plane have a close-form solution. Thus we can eliminate planes from the cost function, which significantly reduces the number of variables. Furthermore, as the optimal planes are functions of poses, this method actually ensures that the optimal planes for the current estimated poses can be obtained at each iteration, which benefits the convergence. The difficulty lies in how to efficiently compute the Hessian matrix and the gradient of the resulting cost. This paper provides an efficient solution. Empirical evaluation shows that our algorithm outperforms the state-of-the-art algorithms.

关键词： 3d from multi-view and sensors

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In-Hand 3d Object Scanning from an RGB Sequence

In-Hand 3D Object Scanning from an RGB Sequence

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

作者： Hampali, Shreyas Hodan, Tomas Tran, Luan Ma, Lingni Keskin, Cem Lepetit, Vincent Real Labs Meta Menlo Pk CA USA Univ Gustave Eiffel CNRS LIGM Ecole Ponts Marne La Vallee France Graz Univ Technol Inst Comp Graph & Vis Graz Austria

ISBN: (纸本)9798350301298

We propose a method for in-hand 3d scanning of an unknown object with a monocular camera. Our method relies on a neural implicit surface representation that captures both the geometry and the appearance of the object, however, by contrast with most NeRF-based methods, we do not assume that the camera-object relative poses are known. Instead, we simultaneously optimize both the object shape and the pose trajectory. As direct optimization over all shape and pose parameters is prone to fail without coarse-level initialization, we propose an incremental approach that starts by splitting the sequence into carefully selected overlapping segments within which the optimization is likely to succeed. We reconstruct the object shape and track its poses independently within each segment, then merge all the segments before performing a global optimization. We show that our method is able to reconstruct the shape and color of both textured and challenging texture-less objects, outperforms classical methods that rely only on appearance features, and that its performance is close to recent methods that assume known camera poses.

关键词： 3d from multi-view and sensors

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