title: AutoRecon: Automated 3D Object Discovery and Reconstruction
summary: 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.
paper_authors._id: 64630dddc9cc74e82e288e68
paper_authors.name: Yuang Wang
paper_authors._id: 64630dddc9cc74e82e288e69
paper_authors.name: Xingyi He
paper_authors._id: 64630dddc9cc74e82e288e6a
paper_authors.name: Sida Peng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-05-24T08:48:12.076Z
paper_authors._id: 62986ca2b58e71e2ac9b8f01
paper_authors.avatarUrl: /avatars/83944db5f3dbb6f47c47c46fb2cb2849.svg
paper_authors.fullname: Sida Peng
paper_authors.type: user
paper_authors.user: pengsida
paper_authors._id: 64630dddc9cc74e82e288e6b
paper_authors.name: Haotong Lin
paper_authors._id: 64630dddc9cc74e82e288e6c
paper_authors.name: Hujun Bao
paper_authors._id: 64630dddc9cc74e82e288e6d
paper_authors.name: Xiaowei Zhou
paper_title: AutoRecon: Automated 3D Object Discovery and Reconstruction
paper_summary: 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.
paper_ai_keywords: self-supervised 2D vision transformer SfM point clouds neural scene representations dense supervision decomposed point clouds