AutoRecon: Automated 3D Object Discovery and Reconstruction
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.
64630dddc9cc74e82e288e68
Yuang Wang
claimed_verified
2025-08-19T10:52:32.196Z
6422dd402f38c0a50cfd5405
/avatars/d3943b7b34c8026fa26eee3837590260.svg
Yuang Wang
user
angshineee
64630dddc9cc74e82e288e69
Xingyi He
64630dddc9cc74e82e288e6a
Sida Peng
admin_assigned
2023-05-24T08:48:12.076Z
62986ca2b58e71e2ac9b8f01
/avatars/83944db5f3dbb6f47c47c46fb2cb2849.svg
Sida Peng
user
pengsida
64630dddc9cc74e82e288e6b
Haotong Lin
64630dddc9cc74e82e288e6c
Hujun Bao
64630dddc9cc74e82e288e6d
Xiaowei Zhou
AutoRecon: Automated 3D Object Discovery and Reconstruction
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.
AutoRecon automates object reconstruction from multi-view images using self-supervised 2D vision transformers and dense supervision from decomposed point clouds.
self-supervised 2D vision transformer neural scene representations SfM point clouds