Single-View Shape Completion for Robotic Grasping in Clutter

TL;DR

This paper introduces a diffusion model-based method for 3D shape completion from a single view to improve robotic grasping in cluttered environments, showing significant success rate improvements over baselines.

Contribution

It presents a novel category-level 3D shape completion approach using diffusion models for cluttered scene grasping, outperforming existing methods.

Findings

23% higher grasp success rate than baseline without shape completion

19% improvement over recent state-of-the-art shape completion methods

Effective in realistic cluttered household object scenarios

Abstract

In vision-based robot manipulation, a single camera view can only capture one side of objects of interest, with additional occlusions in cluttered scenes further restricting visibility. As a result, the observed geometry is incomplete, and grasp estimation algorithms perform suboptimally. To address this limitation, we leverage diffusion models to perform category-level 3D shape completion from partial depth observations obtained from a single view, reconstructing complete object geometries to provide richer context for grasp planning. Our method focuses on common household items with diverse geometries, generating full 3D shapes that serve as input to downstream grasp inference networks. Unlike prior work, which primarily considers isolated objects or minimal clutter, we evaluate shape completion and grasping in realistic clutter scenarios with household objects. In preliminary evaluations on a cluttered scene, our approach consistently results in better grasp success rates than a naive baseline without shape completion by 23% and over a recent state of the art shape completion approach by 19%. Our code is available at https://amm.aass.oru.se/shape-completion-grasping/.