Novel Object 6D Pose Estimation with a Single Reference View

• URL: http://arxiv.org/abs/2503.05578v1
• Date: Fri, 07 Mar 2025 17:00:41 GMT
• Title: Novel Object 6D Pose Estimation with a Single Reference View
• Authors: Jian Liu, Wei Sun, Kai Zeng, Jin Zheng, Hui Yang, Lin Wang, Hossein Rahmani, Ajmal Mian,
• Abstract summary: We propose a Single-Reference-based novel object 6D (SinRef-6D) pose estimation method.<n>Our key idea is to iteratively establish point-wise alignment in the camera coordinate system based on state space models (SSMs)<n>Once pre-trained on synthetic data, SinRef-6D can estimate the 6D pose of a novel object using only a single reference view.
• Score: 39.226579637659235
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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