title: Novel Object 6D Pose Estimation with a Single Reference View
summary: Existing novel object 6D pose estimation methods typically rely on CAD models
  or dense reference views, which are both difficult to acquire. Using only a
  single reference view is more scalable, but challenging due to large pose
  discrepancies and limited geometric and spatial information. To address these
  issues, we propose a Single-Reference-based novel object 6D (SinRef-6D) pose
  estimation method. Our key idea is to iteratively establish point-wise
  alignment in the camera coordinate system based on state space models (SSMs).
  Specifically, iterative camera-space point-wise alignment can effectively
  handle large pose discrepancies, while our proposed RGB and Points SSMs can
  capture long-range dependencies and spatial information from a single view,
  offering linear complexity and superior spatial modeling capability. Once
  pre-trained on synthetic data, SinRef-6D can estimate the 6D pose of a novel
  object using only a single reference view, without requiring retraining or a
  CAD model. Extensive experiments on six popular datasets and real-world robotic
  scenes demonstrate that we achieve on-par performance with CAD-based and dense
  reference view-based methods, despite operating in the more challenging single
  reference setting. Code will be released at
  https://github.com/CNJianLiu/SinRef-6D.
paper_authors._id: 67d01e7b66fafa3aa91677e1
paper_authors.name: Jian Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-11T11:43:04.337Z
paper_authors._id: 67d01cc5211e07ebcf89efd0
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/P2Czu4w0Zz-POjteCtEm_.jpeg
paper_authors.fullname: Jian Liu
paper_authors.type: user
paper_authors.user: JianLiu99
paper_authors._id: 67d01e7b66fafa3aa91677e2
paper_authors.name: Wei Sun
paper_authors._id: 67d01e7b66fafa3aa91677e3
paper_authors.name: Kai Zeng
paper_authors._id: 67d01e7b66fafa3aa91677e4
paper_authors.name: Jin Zheng
paper_authors._id: 67d01e7b66fafa3aa91677e5
paper_authors.name: Hui Yang
paper_authors._id: 67d01e7b66fafa3aa91677e6
paper_authors.name: Lin Wang
paper_authors._id: 67d01e7b66fafa3aa91677e7
paper_authors.name: Hossein Rahmani
paper_authors._id: 67d01e7b66fafa3aa91677e8
paper_authors.name: Ajmal Mian
paper_title: Novel Object 6D Pose Estimation with a Single Reference View
paper_summary: Existing novel object 6D pose estimation methods typically rely on CAD models
  or dense reference views, which are both difficult to acquire. Using only a
  single reference view is more scalable, but challenging due to large pose
  discrepancies and limited geometric and spatial information. To address these
  issues, we propose a Single-Reference-based novel object 6D (SinRef-6D) pose
  estimation method. Our key idea is to iteratively establish point-wise
  alignment in the camera coordinate system based on state space models (SSMs).
  Specifically, iterative camera-space point-wise alignment can effectively
  handle large pose discrepancies, while our proposed RGB and Points SSMs can
  capture long-range dependencies and spatial information from a single view,
  offering linear complexity and superior spatial modeling capability. Once
  pre-trained on synthetic data, SinRef-6D can estimate the 6D pose of a novel
  object using only a single reference view, without requiring retraining or a
  CAD model. Extensive experiments on six popular datasets and real-world robotic
  scenes demonstrate that we achieve on-par performance with CAD-based and dense
  reference view-based methods, despite operating in the more challenging single
  reference setting. Code will be released at
  https://github.com/CNJianLiu/SinRef-6D.
paper_ai_keywords: state space models (SSMs) iterative camera-space point-wise alignment RGB and Points SSMs long-range dependencies spatial modeling capability pre-trained synthetic data 6D pose estimation novel object 6D estimation single reference view CAD-based methods dense reference view-based methods real-world robotic scenes