PlaneMVS: 3D Plane Reconstruction from Multi-View Stereo
Jiachen Liu, Pan Ji, Nitin Bansal, Changjiang Cai, Qingan Yan, Xiaolei, Huang, Yi Xu

TL;DR
PlaneMVS introduces an innovative multi-view stereo framework for 3D plane reconstruction that leverages multi-view geometry and plane priors, outperforming existing single-view and MVS methods in accuracy.
Contribution
This work is the first to integrate 3D plane reconstruction into an end-to-end multi-view stereo pipeline, combining semantic detection and plane sweeping with a novel soft-pooling loss.
Findings
Outperforms state-of-the-art single-view plane reconstruction methods.
Surpasses existing MVS methods due to learned plane priors.
Achieves superior 3D geometry accuracy on indoor datasets.
Abstract
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…
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Taxonomy
TopicsAdvanced Vision and Imaging · Optical measurement and interference techniques · Robotics and Sensor-Based Localization
