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Point-Based Multi-View Stereo Network
TL;DR: This work introduces Point-MVSNet, a novel point-based deep framework for multi-view stereo (MVS), which directly processes the target scene as point clouds and allows higher accuracy, more computational efficiency and more flexibility than cost-volume-based counterparts.
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Abstract: We introduce Point-MVSNet, a novel point-based deep framework for multi-view stereo (MVS). Distinct from existing cost volume approaches, our method directly processes the target scene as point clouds. More specifically, our method predicts the depth in a coarse-to-fine manner. We first generate a coarse depth map, convert it into a point cloud and refine the point cloud iteratively by estimating the residual between the depth of the current iteration and that of the ground truth. Our network leverages 3D geometry priors and 2D texture information jointly and effectively by fusing them into a feature-augmented point cloud, and processes the point cloud to estimate the 3D flow for each point. This point-based architecture allows higher accuracy, more computational efficiency and more flexibility than cost-volume-based counterparts. Experimental results show that our approach achieves a significant improvement in reconstruction quality compared with state-of-the-art methods on the DTU and the Tanks and Temples dataset. Our source code and trained models are available at this https URL .
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Citations
Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matching
Xiaodong Gu,Zhiwen Fan,Siyu Zhu,Zuozhuo Dai,Feitong Tan,Ping Tan +5 more
- 14 Jun 2020
TL;DR: This paper proposes a both memory and time efficient cost volume formulation that is complementary to existing multi-view stereo and stereo matching approaches based on 3D cost volumes and applies the cascade cost volume to the representative MVS-Net, obtaining a 35.6% improvement on DTU benchmark.
Deep Stereo Using Adaptive Thin Volume Representation With Uncertainty Awareness
Shuo Cheng,Zexiang Xu,Shilin Zhu,Zhuwen Li,Li Erran Li,Ravi Ramamoorthi,Hao Su +6 more
- 14 Jun 2020
TL;DR: The proposed ATV consists of only a small number of planes with low memory and computation costs; yet, it efficiently partitions local depth ranges within learned small uncertainty intervals, which enables reconstruction with high completeness and accuracy in a coarse-to-fine fashion.
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Neural Reflectance Fields for Appearance Acquisition
Sai Bi,Zexiang Xu,Pratul P. Srinivasan,Ben Mildenhall,Kalyan Sunkavalli,Miloš Hašan,Yannick Hold-Geoffroy,David J. Kriegman,Ravi Ramamoorthi +8 more
TL;DR: It is demonstrated that neural reflectance fields can be estimated from images captured with a simple collocated camera-light setup, and accurately model the appearance of real-world scenes with complex geometry and reflectance, and enable a complete pipeline from high-quality and practical appearance acquisition to 3D scene composition and rendering.
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PatchmatchNet: Learned Multi-View Patchmatch Stereo
TL;DR: For the first time, an iterative multi-scale Patchmatch in an end-to-end trainable architecture is introduced and the Patchmatch core algorithm is improved with a novel and learned adaptive propagation and evaluation scheme for each iteration.
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Dense Hybrid Recurrent Multi-view Stereo Net with Dynamic Consistency Checking
Jianfeng Yan,Zizhuang Wei,Hongwei Yi,Mingyu Ding,Runze Zhang,Yisong Chen,Guoping Wang,Yu-Wing Tai +7 more
- 23 Aug 2020
TL;DR: An efficient and effective dense hybrid recurrent multi-view stereo net with dynamic consistency checking, namely D^{2}$HC-RMVSNet, for accurate dense point cloud reconstruction and dynamically aggregate geometric consistency matching error among all the views is proposed.
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