Object Pose and Shape Estimation for Grasping: Does it Work?
The problem of object pose and shape estimation has seen key advancements lately. Encoder-decoder (e.g., SAM3D, LRM, CRISP) and diffusion-based models (e.g., InstantMesh, Zero123, SceneComplete) have shown category-agnostic shape encoding capacity and open-set generalizability. In this work, we ask the question: Are the object pose and shape estimation methods mature enough, such that when used with antipodal grasp sampling, can outperform the end-to-end grasp synthesis methods? We explore this question in detail by scoping our study to parallel jaw grippers, 7-DoF grasps, and single-view RGB(-D) image as input. We implement and compare a state-of-the-art, end-to-end grasp synthesis metho...
| Authors | Pavan Karke, Kushal Shah, Gaurav Singh et al. |
|---|---|
| Primary category | cs.RO |
| Published | 26 May 2026 |
| Last updated at source | 26 May 2026 |
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