PlaneMVS: 3D Plane Reconstruction from Multi-View Stereo
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866916276087029760 |
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| author | Liu, Jiachen Ji, Pan Bansal, Nitin Cai, Changjiang Yan, Qingan Huang, Xiaolei Xu, Yi |
| author_facet | Liu, Jiachen Ji, Pan Bansal, Nitin Cai, Changjiang Yan, Qingan Huang, Xiaolei Xu, Yi |
| contents | We present a novel framework named PlaneMVS for 3D plane reconstruction from multiple input views with known camera poses. Most previous learning-based plane reconstruction methods reconstruct 3D planes from single images, which highly rely on single-view regression and suffer from depth scale ambiguity. In contrast, we reconstruct 3D planes with a multi-view-stereo (MVS) pipeline that takes advantage of multi-view geometry. We decouple plane reconstruction into a semantic plane detection branch and a plane MVS branch. The semantic plane detection branch is based on a single-view plane detection framework but with differences. The plane MVS branch adopts a set of slanted plane hypotheses to replace conventional depth hypotheses to perform plane sweeping strategy and finally learns pixel-level plane parameters and its planar depth map. We present how the two branches are learned in a balanced way, and propose a soft-pooling loss to associate the outputs of the two branches and make them benefit from each other. Extensive experiments on various indoor datasets show that PlaneMVS significantly outperforms state-of-the-art (SOTA) single-view plane reconstruction methods on both plane detection and 3D geometry metrics. Our method even outperforms a set of SOTA learning-based MVS methods thanks to the learned plane priors. To the best of our knowledge, this is the first work on 3D plane reconstruction within an end-to-end MVS framework. Source code: https://github.com/oppo-us-research/PlaneMVS. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2203_12082 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | PlaneMVS: 3D Plane Reconstruction from Multi-View Stereo Liu, Jiachen Ji, Pan Bansal, Nitin Cai, Changjiang Yan, Qingan Huang, Xiaolei Xu, Yi Computer Vision and Pattern Recognition We present a novel framework named PlaneMVS for 3D plane reconstruction from multiple input views with known camera poses. Most previous learning-based plane reconstruction methods reconstruct 3D planes from single images, which highly rely on single-view regression and suffer from depth scale ambiguity. In contrast, we reconstruct 3D planes with a multi-view-stereo (MVS) pipeline that takes advantage of multi-view geometry. We decouple plane reconstruction into a semantic plane detection branch and a plane MVS branch. The semantic plane detection branch is based on a single-view plane detection framework but with differences. The plane MVS branch adopts a set of slanted plane hypotheses to replace conventional depth hypotheses to perform plane sweeping strategy and finally learns pixel-level plane parameters and its planar depth map. We present how the two branches are learned in a balanced way, and propose a soft-pooling loss to associate the outputs of the two branches and make them benefit from each other. Extensive experiments on various indoor datasets show that PlaneMVS significantly outperforms state-of-the-art (SOTA) single-view plane reconstruction methods on both plane detection and 3D geometry metrics. Our method even outperforms a set of SOTA learning-based MVS methods thanks to the learned plane priors. To the best of our knowledge, this is the first work on 3D plane reconstruction within an end-to-end MVS framework. Source code: https://github.com/oppo-us-research/PlaneMVS. |
| title | PlaneMVS: 3D Plane Reconstruction from Multi-View Stereo |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2203.12082 |