Pseudo-Plane Regularized Signed Distance Field for Neural Indoor Scene Reconstruction.
Saved in:
| Title: | Pseudo-Plane Regularized Signed Distance Field for Neural Indoor Scene Reconstruction. |
|---|---|
| Authors: | Li, Jing1,2 (AUTHOR) lijing1@alumni.shanghaitech.edu.cn, Yu, Jinpeng1,2 (AUTHOR) yujp1@shanghaitech.edu.cn, Wang, Ruoyu1 (AUTHOR) wangry3@shanghaitech.edu.cn, Gao, Shenghua3 (AUTHOR) gaosh@hku.hk |
| Source: | International Journal of Computer Vision. Jun2025, Vol. 133 Issue 6, p3203-3221. 19p. |
| Subjects: | Surface reconstruction, Pixels, Signals & signaling |
| Abstract: | Given only a set of images, neural implicit surface representation has shown its capability in 3D surface reconstruction. However, as the nature of per-scene optimization is based on the volumetric rendering of color, previous neural implicit surface reconstruction methods usually fail in the low-textured regions, including floors, walls, etc., which commonly exist for indoor scenes. Being aware of the fact that these low-textured regions usually correspond to planes, without introducing additional ground-truth supervisory signals or making additional assumptions about the room layout, we propose to leverage a novel Pseudo-plane regularized Signed Distance Field (PPlaneSDF) for indoor scene reconstruction. Specifically, we consider adjacent pixels with similar colors to be on the same pseudo-planes. The plane parameters are then estimated on the fly during training by an efficient and effective two-step scheme. Then the signed distances of the points on the planes are regularized by the estimated plane parameters in the training phase. As the unsupervised plane segments are usually noisy and inaccurate, we propose to assign different weights to the sampled points on the plane in plane estimation as well as the regularization loss. The weights come by fusing the plane segments from different views. As the sampled rays in the planar regions are redundant, leading to inefficient training, we further propose a keypoint-guided rays sampling strategy that attends to the informative textured regions with large color variations, and the implicit network gets a better reconstruction, compared with the original uniform ray sampling strategy. Experiments show that our PPlaneSDF achieves competitive reconstruction performance in Manhattan scenes. Further, as we do not introduce any additional room layout assumption, our PPlaneSDF generalizes well to the reconstruction of non-Manhattan scenes. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Computer Vision is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | Given only a set of images, neural implicit surface representation has shown its capability in 3D surface reconstruction. However, as the nature of per-scene optimization is based on the volumetric rendering of color, previous neural implicit surface reconstruction methods usually fail in the low-textured regions, including floors, walls, etc., which commonly exist for indoor scenes. Being aware of the fact that these low-textured regions usually correspond to planes, without introducing additional ground-truth supervisory signals or making additional assumptions about the room layout, we propose to leverage a novel Pseudo-plane regularized Signed Distance Field (PPlaneSDF) for indoor scene reconstruction. Specifically, we consider adjacent pixels with similar colors to be on the same pseudo-planes. The plane parameters are then estimated on the fly during training by an efficient and effective two-step scheme. Then the signed distances of the points on the planes are regularized by the estimated plane parameters in the training phase. As the unsupervised plane segments are usually noisy and inaccurate, we propose to assign different weights to the sampled points on the plane in plane estimation as well as the regularization loss. The weights come by fusing the plane segments from different views. As the sampled rays in the planar regions are redundant, leading to inefficient training, we further propose a keypoint-guided rays sampling strategy that attends to the informative textured regions with large color variations, and the implicit network gets a better reconstruction, compared with the original uniform ray sampling strategy. Experiments show that our PPlaneSDF achieves competitive reconstruction performance in Manhattan scenes. Further, as we do not introduce any additional room layout assumption, our PPlaneSDF generalizes well to the reconstruction of non-Manhattan scenes. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 09205691 |
| DOI: | 10.1007/s11263-024-02319-w |
