AutoRecon: Automated 3D Object Discovery and Reconstruction
Yuang Wang Xingyi He Sida Peng Haotong Lin Hujun Bao Xiaowei Zhou
A fully automated object reconstruction pipeline is crucial for digital
content creation. While the area of 3D reconstruction has witnessed profound
developments, the removal of background to obtain a clean object model still
relies on different forms of manual labor, such as bounding box labeling, mask
annotations, and mesh manipulations. In this paper, we propose a novel
framework named AutoRecon for the automated discovery and reconstruction of an
object from multi-view images. We demonstrate that foreground objects can be
robustly located and segmented from SfM point clouds by leveraging
self-supervised 2D vision transformer features. Then, we reconstruct decomposed
neural scene representations with dense supervision provided by the decomposed
point clouds, resulting in accurate object reconstruction and segmentation.
Experiments on the DTU, BlendedMVS and CO3D-V2 datasets demonstrate the
effectiveness and robustness of AutoRecon.