Single-View Shape Completion for Robotic Grasping in Clutter

• URL: http://arxiv.org/abs/2512.16449v1
• Date: Thu, 18 Dec 2025 12:11:05 GMT
• Title: Single-View Shape Completion for Robotic Grasping in Clutter
• Abstract summary: In vision-based robot manipulation, a single camera view can only capture one side of objects of interest.<n>We leverage diffusion models to perform category-level 3D shape completion from partial depth observations.<n>Our approach consistently results in better grasp success rates than a naive baseline without shape completion by 23%.
• Score: 5.084516012213421
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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/.

Related papers

• Towards Flexible 3D Perception: Object-Centric Occupancy Completion Augments 3D Object Detection [54.78470057491049]
  Occupancy has emerged as a promising alternative for 3D scene perception.<n>We introduce object-centric occupancy as a supplement to object bboxes.<n>We show that our occupancy features significantly enhance the detection results of state-of-the-art 3D object detectors.
  arXiv  Detail & Related papers  (2024-12-06T16:12:38Z)
• Local Occupancy-Enhanced Object Grasping with Multiple Triplanar Projection [24.00828999360765]
  This paper addresses the challenge of robotic grasping of general objects. The proposed model first runs by proposing a number of most likely grasp points in the scene. Around each grasp point, a module is designed to infer any voxel in its neighborhood to be either void or occupied by some object. The model further estimates 6-DoF grasp poses utilizing the local occupancy-enhanced object shape information.
  arXiv  Detail & Related papers  (2024-07-22T16:22:28Z)
• Unsupervised 3D Point Cloud Completion via Multi-view Adversarial Learning [61.14132533712537]
  We propose MAL-UPC, a framework that effectively leverages both region-level and category-specific geometric similarities to complete missing structures.<n>Our MAL-UPC does not require any 3D complete supervision and only necessitates single-view partial observations in the training set.
  arXiv  Detail & Related papers  (2024-07-13T06:53:39Z)
• Robust 3D Tracking with Quality-Aware Shape Completion [67.9748164949519]
  We propose a synthetic target representation composed of dense and complete point clouds depicting the target shape precisely by shape completion for robust 3D tracking. Specifically, we design a voxelized 3D tracking framework with shape completion, in which we propose a quality-aware shape completion mechanism to alleviate the adverse effect of noisy historical predictions.
  arXiv  Detail & Related papers  (2023-12-17T04:50:24Z)
• CenterGrasp: Object-Aware Implicit Representation Learning for Simultaneous Shape Reconstruction and 6-DoF Grasp Estimation [24.20283483897243]
  We introduce CenterGrasp, a novel framework that combines object awareness and holistic grasping. CenterGrasp learns a general object prior by encoding shapes and valid grasps in a continuous latent space. Compared to the state of the art, CenterGrasp achieves an improvement of 38.5 mm in shape reconstruction and 33 percentage points on average in grasp success.
  arXiv  Detail & Related papers  (2023-12-13T16:01:50Z)
• Towards Confidence-guided Shape Completion for Robotic Applications [6.940242990198]
  Deep learning has begun taking traction as effective means of inferring a complete 3D object representation from partial visual data. We propose an object shape completion method based on an implicit 3D representation providing a confidence value for each reconstructed point. We experimentally validate our approach by comparing reconstructed shapes with ground truths, and by deploying our shape completion algorithm in a robotic grasping pipeline.
  arXiv  Detail & Related papers  (2022-09-09T13:48:24Z)
• ShAPO: Implicit Representations for Multi-Object Shape, Appearance, and Pose Optimization [40.36229450208817]
  We present ShAPO, a method for joint multi-object detection, 3D textured reconstruction, 6D object pose and size estimation. Key to ShAPO is a single-shot pipeline to regress shape, appearance and pose latent codes along with the masks of each object instance. Our method significantly out-performs all baselines on the NOCS dataset with an 8% absolute improvement in mAP for 6D pose estimation.
  arXiv  Detail & Related papers  (2022-07-27T17:59:31Z)
• From Points to Multi-Object 3D Reconstruction [71.17445805257196]
  We propose a method to detect and reconstruct multiple 3D objects from a single RGB image. A keypoint detector localizes objects as center points and directly predicts all object properties, including 9-DoF bounding boxes and 3D shapes. The presented approach performs lightweight reconstruction in a single-stage, it is real-time capable, fully differentiable and end-to-end trainable.
  arXiv  Detail & Related papers  (2020-12-21T18:52:21Z)
• A Divide et Impera Approach for 3D Shape Reconstruction from Multiple Views [49.03830902235915]
  Estimating the 3D shape of an object from a single or multiple images has gained popularity thanks to the recent breakthroughs powered by deep learning. This paper proposes to rely on viewpoint variant reconstructions by merging the visible information from the given views. To validate the proposed method, we perform a comprehensive evaluation on the ShapeNet reference benchmark in terms of relative pose estimation and 3D shape reconstruction.
  arXiv  Detail & Related papers  (2020-11-17T09:59:32Z)
• Single View Metrology in the Wild [94.7005246862618]
  We present a novel approach to single view metrology that can recover the absolute scale of a scene represented by 3D heights of objects or camera height above the ground. Our method relies on data-driven priors learned by a deep network specifically designed to imbibe weakly supervised constraints from the interplay of the unknown camera with 3D entities such as object heights. We demonstrate state-of-the-art qualitative and quantitative results on several datasets as well as applications including virtual object insertion.
  arXiv  Detail & Related papers  (2020-07-18T22:31:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.