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

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FAC: 3d Representation Learning via Foreground Aware Feature Contrast

FAC: 3D Representation Learning via Foreground Aware Feature...

引用

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

作者： Liu, Kangcheng Xiao, Aoran Zhang, Xiaoqin Lu, Shijian Shao, Ling Nanyang Technol Univ Singapore Singapore Wenzhou Univ Wenzhou Peoples R China UCAS UCAS Terminus Lab Cheltenham Glos England

ISBN: (纸本)9798350301298

Contrastive learning has recently demonstrated great potential for unsupervised pre-training in 3d scene understanding tasks. However, most existing work randomly selects point features as anchors while building contrast, leading to a clear bias toward background points that often dominate in 3d scenes. Also, object awareness and foreground-to-background discrimination are neglected, making contrastive learning less effective. To tackle these issues, we propose a general foreground-aware feature contrast (FAC) framework to learn more effective point cloud representations in pre-training. FAC consists of two novel contrast designs to construct more effective and informative contrast pairs. The first is building positive pairs within the same foreground segment where points tend to have the same semantics. The second is that we prevent over-discrimination between 3d segments/objects and encourage foreground-to-background distinctions at the segment level with adaptive feature learning in a Siamese correspondence network, which adaptively learns feature correlations within and across point cloud views effectively. Visualization with point activation maps shows that our contrast pairs capture clear correspondences among foreground regions during pre-training. Quantitative experiments also show that FAC achieves superior knowledge transfer and data efficiency in various downstream 3d semantic segmentation and object detection tasks. All codes, data, and models are available.

关键词： 3d from multi-view and sensors

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PET-NeuS: Positional Encoding Tri-Planes for Neural Surfaces

PET-NeuS: Positional Encoding Tri-Planes for Neural Surfaces

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

作者： Wang, Yiqun Skorokhodov, Ivan Wonka, Peter Chongqing Univ Chongqing Peoples R China KAUST Thuwal Saudi Arabia

ISBN: (纸本)9798350301298

A signed distance function (SdF) parametrized by an MLP is a common ingredient of neural surface reconstruction. We build on the successful recent method NeuS to extend it by three new components. The first component is to borrow the tri-plane representation from EG3d and represent signed distance fields as a mixture of tri-planes and MLPs instead of representing it with MLPs only. Using tri-planes leads to a more expressive data structure but will also introduce noise in the reconstructed surface. The second component is to use a new type of positional encoding with learnable weights to combat noise in the reconstruction process. We divide the features in the tri-plane into multiple frequency scales and modulate them with sin and cos functions of different frequencies. The third component is to use learnable convolution operations on the tri-plane features using self-attention convolution to produce features with different frequency bands. The experiments show that PET-NeuS achieves high-fidelity surface reconstruction on standard datasets. Following previous work and using the Chamfer metric as the most important way to measure surface reconstruction quality, we are able to improve upon the NeuS baseline by 57% on Nerf-synthetic (0.84 compared to 1.97) and by 15.5% on dTU (0.71 compared to 0.84). The qualitative evaluation reveals how our method can better control the interference of high-frequency noise.

关键词： 3d from multi-view and sensors

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SHS-Net: Learning Signed Hyper Surfaces for Oriented Normal Estimation of Point Clouds

SHS-Net: Learning Signed Hyper Surfaces for Oriented Normal ...

引用

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

作者： Li, Qing Feng, Huifang Shi, Kanle Gao, Yue Fang, Yi Liu, Yu-Shen Han, Zhizhong Tsinghua Univ Sch Software BNRist Beijing Peoples R China Xiamen Univ Sch Informat Xiamen Peoples R China Kuaishou Technol Beijing Peoples R China New York Univ Abu Dhabi Ctr Artificial Intelligence & Robot Abu Dhabi U Arab Emirates Wayne State Univ Dept Comp Sci Detroit MI USA

ISBN: (纸本)9798350301298

We propose a novel method called SHS-Net for oriented normal estimation of point clouds by learning signed hyper surfaces, which can accurately predict normals with global consistent orientation from various point clouds. Almost all existing methods estimate oriented normals through a two-stage pipeline, i.e., unoriented normal estimation and normal orientation, and each step is implemented by a separate algorithm. However, previous methods are sensitive to parameter settings, resulting in poor results from point clouds with noise, density variations and complex geometries. In this work, we introduce signed hyper surfaces (SHS), which are parameterized by multi-layer perceptron (MLP) layers, to learn to estimate oriented normals from point clouds in an end-to-end manner. The signed hyper surfaces are implicitly learned in a high-dimensional feature space where the local and global information is aggregated. Specifically, we introduce a patch encoding module and a shape encoding module to encode a 3d point cloud into a local latent code and a global latent code, respectively. Then, an attention-weighted normal prediction module is proposed as a decoder, which takes the local and global latent codes as input to predict oriented normals. Experimental results show that our SHS-Net outperforms the state-of-the-art methods in both unoriented and oriented normal estimation on the widely used benchmarks. The code, data and pretrained models are available at https://***/LeoQLi/SHS-Net. [GRAPHICS] .

关键词： 3d from multi-view and sensors

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PATS: Patch Area Transportation with Subdivision for Local Feature Matching

PATS: Patch Area Transportation with Subdivision for Local F...

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

作者： Ni, Junjie Li, Yijin Huang, Zhaoyang Li, Hongsheng Bao, Hujun Cui, Zhaopeng Zhang, Guofeng Zhejiang Univ State Key Lab CAD&CG Hangzhou Peoples R China ZJU SenseTime Joint Lab 3D Vision Hangzhou Peoples R China Chinese Univ Hong Kong Multimedia Lab Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Local feature matching aims at establishing sparse correspondences between a pair of images. Recently, detector-free methods present generally better performance but are not satisfactory in image pairs with large scale differences. In this paper, we propose Patch Area Transportation with Subdivision (PATS) to tackle this issue. Instead of building an expensive image pyramid, we start by splitting the original image pair into equal-sized patches and gradually resizing and subdividing them into smaller patches with the same scale. However, estimating scale differences between these patches is non-trivial since the scale differences are determined by both relative camera poses and scene structures, and thus spatially varying over image pairs. Moreover, it is hard to obtain the ground truth for real scenes. To this end, we propose patch area transportation, which enables learning scale differences in a self-supervised manner. In contrast to bipartite graph matching, which only handles one-to-one matching, our patch area transportation can deal with many-to-many relationships. PATS improves both matching accuracy and coverage, and shows superior performance in downstream tasks, such as relative pose estimation, visual localization, and optical flow estimation. The source code is available at https://***/pats/.

关键词： 3d from multi-view and sensors

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SCAdE: NeRFs from Space Carving with Ambiguity-Aware depth Estimates

SCADE: NeRFs from Space Carving with Ambiguity-Aware Depth E...

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

作者： Uy, Mikaela Angelina Martin-Brualla, Ricardo Guibas, Leonidas Li, Ke Stanford Univ Stanford CA 94305 USA Google New York NY USA Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9798350301298

Neural radiance fields (NeRFs) have enabled high fidelity 3d reconstruction from multiple 2d input views. However, a well-known drawback of NeRFs is the less-than-ideal performance under a small number of views, due to insufficient constraints enforced by volumetric rendering. To address this issue, we introduce SCAdE, a novel technique that improves NeRF reconstruction quality on sparse, unconstrained input views for in-the-wild indoor scenes. To constrain NeRF reconstruction, we leverage geometric priors in the form of per-view depth estimates produced with state-of-the-art monocular depth estimation models, which can generalize across scenes. A key challenge is that monocular depth estimation is an ill-posed problem, with inherent ambiguities. To handle this issue, we propose a new method that learns to predict, for each view, a continuous, multimodal distribution of depth estimates using conditional Implicit Maximum Likelihood Estimation (cIMLE). In order to disambiguate exploiting multiple views, we introduce an original space carving loss that guides the NeRF representation to fuse multiple hypothesized depth maps from each view and distill from them a common geometry that is consistent with all views. Experiments show that our approach enables higher fidelity novel view synthesis from sparse views. Our project page can be found at ***.

关键词： 3d from multi-view and sensors

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NeudA: Neural deformable Anchor for High-Fidelity Implicit Surface Reconstruction

NeuDA: Neural Deformable Anchor for High-Fidelity Implicit S...

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

作者： Cai, Bowen Huang, Jinchi Jia, Ronglei Lv, Chengfei Fu, Huan Alibaba Grp Tao Technol Dept Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9798350301298

This paper studies implicit surface reconstruction leveraging differentiable ray casting. Previous works such as IdR [34] and NeuS [27] overlook the spatial context in 3d space when predicting and rendering the surface, thereby may fail to capture sharp local topologies such as small holes and structures. To mitigate the limitation, we propose a flexible neural implicit representation leveraging hierarchical voxel grids, namely Neural deformable Anchor (NeudA), for high-fidelity surface reconstruction. NeudA maintains the hierarchical anchor grids where each vertex stores a 3d position (or anchor) instead of the direct embedding (or feature). We optimize the anchor grids such that different local geometry structures can be adaptively encoded. Besides, we dig into the frequency encoding strategies and introduce a simple hierarchical positional encoding method for the hierarchical anchor structure to flexibly exploit the properties of high-frequency and low-frequency geometry and appearance. Experiments on both the dTU [8] and BlendedMVS [32] datasets demonstrate that NeudA can produce promising mesh surfaces.

关键词： 3d from multi-view and sensors

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Progressively Optimized Local Radiance Fields for Robust view Synthesis

Progressively Optimized Local Radiance Fields for Robust Vie...

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

作者： Meuleman, Andreas Liu, Yu-Lun Gao, Chen Huang, Jia-Bin Kim, Changil Kim, Min H. Kopf, Johannes Korea Adv Inst Sci & Technol Daejeon South Korea Natl Taiwan Univ Taipei Taiwan Meta Cambridge MA USA Univ Maryland College Pk MD USA

ISBN: (纸本)9798350301298

We present an algorithm for reconstructing the radiance field of a large-scale scene from a single casually captured video. The task poses two core challenges. First, most existing radiance field reconstruction approaches rely on accurate pre-estimated camera poses from Structure-from-Motion algorithms, which frequently fail on in-the-wild videos. Second, using a single, global radiance field with finite representational capacity does not scale to longer trajectories in an unbounded scene. For handling unknown poses, we jointly estimate the camera poses with radiance field in a progressive manner. We show that progressive optimization significantly improves the robustness of the reconstruction. For handling large unbounded scenes, we dynamically allocate new local radiance fields trained with frames within a temporal window. This further improves robustness (e.g., performs well even under moderate pose drifts) and allows us to scale to large scenes. Our extensive evaluation on the TANKS ANd TEMPLES dataset and our collected outdoor dataset, STATIC HIKES, show that our approach compares favorably with the state-of-the-art.

关键词： 3d from multi-view and sensors

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Binarizing Sparse Convolutional Networks for Efficient Point Cloud Analysis

Binarizing Sparse Convolutional Networks for Efficient Point...

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

作者： Xu, Xiuwei Wang, Ziwei Zhou, Jie Lu, Jiwen Tsinghua Univ Dept Automat Beijing Peoples R China Beijing Natl Res Ctr Informat Sci & Technol Beijing Peoples R China

ISBN: (纸本)9798350301298

In this paper, we propose binary sparse convolutional networks called BSC-Net for efficient point cloud analysis. We empirically observe that sparse convolution operation causes larger quantization errors than standard convolution. However, conventional network quantization methods directly binarize the weights and activations in sparse convolution, resulting in performance drop due to the significant quantization loss. On the contrary, we search the optimal subset of convolution operation that activates the sparse convolution at various locations for quantization error alleviation, and the performance gap between real-valued and binary sparse convolutional networks is closed without complexity overhead. Specifically, we first present the shifted sparse convolution that fuses the information in the receptive field for the active sites that match the pre-defined positions. Then we employ the differentiable search strategies to discover the optimal opsitions for active site matching in the shifted sparse convolution, and the quantization errors are significantly alleviated for efficient point cloud analysis. For fair evaluation of the proposed method, we empirically select the recently advances that are beneficial for sparse convolution network binarization to construct a strong baseline. The experimental results on ScanNet and NYU depth v2 show that our BSC-Net achieves significant improvement upon our srtong baseline and outperforms the state-of-the-art network binarization methods by a remarkable margin without additional computation overhead for binarizing sparse convolutional networks.

关键词： 3d from multi-view and sensors

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ShadowNeuS: Neural SdF Reconstruction by Shadow Ray Supervision

ShadowNeuS: Neural SDF Reconstruction by Shadow Ray Supervis...

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

作者： Ling, Jingwang Wang, Zhibo Xu, Feng Tsinghua Univ Sch Software Beijing Peoples R China Tsinghua Univ BNRist Beijing Peoples R China SenseTime Res Hong Kong Peoples R China

ISBN: (纸本)9798350301298

By supervising camera rays between a scene and multiview image planes, NeRF reconstructs a neural scene representation for the task of novel view synthesis. On the other hand, shadow rays between the light source and the scene have yet to be considered. Therefore, we propose a novel shadow ray supervision scheme that optimizes both the samples along the ray and the ray location. By supervising shadow rays, we successfully reconstruct a neural SdF of the scene from single-view images under multiple lighting conditions. Given single-view binary shadows, we train a neural network to reconstruct a complete scene not limited by the camera's line of sight. By further modeling the correlation between the image colors and the shadow rays, our technique can also be effectively extended to RGB inputs. We compare our method with previous works on challenging tasks of shape reconstruction from singleview binary shadow or RGB images and observe significant improvements. The code and data are available at https://***/gerwang/ShadowNeuS.

关键词： 3d from multi-view and sensors

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Learning Neural Parametric Head Models

Learning Neural Parametric Head Models

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

作者： Giebenhain, Simon Kirschstein, Tobias Georgopoulos, Markos Runz, Martin Agapito, Lourdes Niessner, Matthias Tech Univ Munich Munich Germany Synthesia London England UCL London England

ISBN: (纸本)9798350301298

We propose a novel 3d morphable model for complete human heads based on hybrid neural fields. At the core of our model lies a neural parametric representation that disentangles identity and expressions in disjoint latent spaces. To this end, we capture a person's identity in a canonical space as a signed distance field (SdF), and model facial expressions with a neural deformation field. In addition, our representation achieves high-fidelity local detail by introducing an ensemble of local fields centered around facial anchor points. To facilitate generalization, we train our model on a newly-captured dataset of over 3700 head scans from 203 different identities using a custom high-end 3d scanning setup. Our dataset significantly exceeds comparable existing datasets, both with respect to quality and completeness of geometry, averaging around 3.5M mesh faces per scan1. Finally, we demonstrate that our approach outperforms state-of-the-art methods in terms of fitting error and reconstruction quality.

关键词： 3d from multi-view and sensors

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