Neural 3D Scene Reconstruction with the Manhattan-world Assumption

CVPR 2022 (Oral Presentation)


Haoyu Guo1*, Sida Peng1*, Haotong Lin1, Qianqian Wang2, Guofeng Zhang1, Hujun Bao1, Xiaowei Zhou1

1Zhejiang University    2Cornell University
* denotes equal contribution

Abstract


This paper addresses the challenge of reconstructing 3D indoor scenes from multi-view images. Many previous works have shown impressive reconstruction results on textured objects, but they still have difficulty in handling low-textured planar regions, which are common in indoor scenes. An approach to solving this issue is to incorporate planer constraints into the depth map estimation in multi-view stereo-based methods, but the per-view plane estimation and depth optimization lack both efficiency and multi-view consistency. In this work, we show that the planar constraints can be conveniently integrated into the recent implicit neural representation-based reconstruction methods. Specifically, we use an MLP network to represent the signed distance function as the scene geometry. Based on the Manhattan-world assumption, planar constraints are employed to regularize the geometry in floor and wall regions predicted by a 2D semantic segmentation network. To resolve the inaccurate segmentation, we encode the semantics of 3D points with another MLP and design a novel loss that jointly optimizes the scene geometry and semantics in 3D space. Experiments on ScanNet and 7-Scenes datasets show that the proposed method outperforms previous methods by a large margin on 3D reconstruction quality.


Overview video



Reconstruction showcase



Zoom in by scrolling. You can toggle the “Single Sided” option in Model Inspector (pressing I key) to enable back-face culling (see through walls). Select “Matcap” to inspect the geometry without textures.


Ablation studies



Comparison with state-of-the-art methods



Citation



@inproceedings{guo2022manhattan,
  title={Neural 3D Scene Reconstruction with the Manhattan-world Assumption},
  author={Guo, Haoyu and Peng, Sida and Lin, Haotong and Wang, Qianqian and Zhang, Guofeng and Bao, Hujun and Zhou, Xiaowei},
  booktitle={CVPR},
  year={2022}
}