Reassembling Broken Objects using Breaking Curves

• URL: http://arxiv.org/abs/2306.02782v1
• Date: Mon, 5 Jun 2023 11:16:50 GMT
• Title: Reassembling Broken Objects using Breaking Curves
• Authors: Ali Alagrami, Luca Palmieri, Sinem Aslan, Marcello Pelillo, Sebastiano Vascon
• Abstract summary: A robust solution that generalizes well must deal with diverse patterns associated with different types of broken objects. We propose a method that tackles the pairwise assembly of 3D point clouds, that is agnostic on the type of object. Results show that our solution performs well in reassembling different kinds of broken objects.
• Score: 11.679900805394816
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Reassembling 3D broken objects is a challenging task. A robust solution that generalizes well must deal with diverse patterns associated with different types of broken objects. We propose a method that tackles the pairwise assembly of 3D point clouds, that is agnostic on the type of object, and that relies solely on their geometrical information, without any prior information on the shape of the reconstructed object. The method receives two point clouds as input and segments them into regions using detected closed boundary contours, known as breaking curves. Possible alignment combinations of the regions of each broken object are evaluated and the best one is selected as the final alignment. Experiments were carried out both on available 3D scanned objects and on a recent benchmark for synthetic broken objects. Results show that our solution performs well in reassembling different kinds of broken objects.

Related papers

• OGC: Unsupervised 3D Object Segmentation from Rigid Dynamics of Point Clouds [4.709764624933227]
  We propose the first unsupervised method, called OGC, to simultaneously identify multiple 3D objects in a single forward pass. We extensively evaluate our method on five datasets, demonstrating the superior performance for object part instance segmentation.
  arXiv  Detail & Related papers  (2022-10-10T07:01:08Z)
• Unsupervised Kinematic Motion Detection for Part-segmented 3D Shape Collections [14.899075941080541]
  We present an unsupervised approach for discovering articulated motions in a part-segmented 3D shape collection. Our approach is based on a concept we call category closure: any valid articulation of an object's parts should keep the object in the same semantic category. We evaluate our approach by using it to re-discover part motions from the PartNet-Mobility dataset.
  arXiv  Detail & Related papers  (2022-06-17T00:50:36Z)
• Topologically Persistent Features-based Object Recognition in Cluttered Indoor Environments [1.2691047660244335]
  Recognition of occluded objects in unseen indoor environments is a challenging problem for mobile robots. This work proposes a new slicing-based topological descriptor that captures the 3D shape of object point clouds. It yields similarities between the descriptors of the occluded and the corresponding unoccluded objects, enabling object unity-based recognition.
  arXiv  Detail & Related papers  (2022-05-16T07:01:16Z)
• Approximate Convex Decomposition for 3D Meshes with Collision-Aware Concavity and Tree Search [23.52274863244624]
  Approximate convex decomposition aims to decompose a 3D shape into a set of almost convex components. It has been widely used in game engines, physics simulations, and animation. We propose a novel method that addresses the limitations of existing approaches from three perspectives.
  arXiv  Detail & Related papers  (2022-05-05T23:40:15Z)
• Discovering 3D Parts from Image Collections [98.16987919686709]
  We tackle the problem of 3D part discovery from only 2D image collections. Instead of relying on manually annotated parts for supervision, we propose a self-supervised approach. Our key insight is to learn a novel part shape prior that allows each part to fit an object shape faithfully while constrained to have simple geometry.
  arXiv  Detail & Related papers  (2021-07-28T20:29:16Z)
• 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)
• Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point Cloud [50.56461318879761]
  We propose Geometry-Disentangled Attention Network (GDANet) for 3D image processing. GDANet disentangles point clouds into contour and flat part of 3D objects, respectively denoted by sharp and gentle variation components. Experiments on 3D object classification and segmentation benchmarks demonstrate that GDANet achieves the state-of-the-arts with fewer parameters.
  arXiv  Detail & Related papers  (2020-12-20T13:35:00Z)
• 3D Object Classification on Partial Point Clouds: A Practical Perspective [91.81377258830703]
  A point cloud is a popular shape representation adopted in 3D object classification. This paper introduces a practical setting to classify partial point clouds of object instances under any poses. A novel algorithm in an alignment-classification manner is proposed in this paper.
  arXiv  Detail & Related papers  (2020-12-18T04:00:56Z)
• Canonical 3D Deformer Maps: Unifying parametric and non-parametric methods for dense weakly-supervised category reconstruction [79.98689027127855]
  We propose a new representation of the 3D shape of common object categories that can be learned from a collection of 2D images of independent objects. Our method builds in a novel way on concepts from parametric deformation models, non-parametric 3D reconstruction, and canonical embeddings. It achieves state-of-the-art results in dense 3D reconstruction on public in-the-wild datasets of faces, cars, and birds.
  arXiv  Detail & Related papers  (2020-08-28T15:44:05Z)
• AutoSweep: Recovering 3D Editable Objectsfrom a Single Photograph [54.701098964773756]
  We aim to recover 3D objects with semantic parts and can be directly edited. Our work makes an attempt towards recovering two types of primitive-shaped objects, namely, generalized cuboids and generalized cylinders. Our algorithm can recover high quality 3D models and outperforms existing methods in both instance segmentation and 3D reconstruction.
  arXiv  Detail & Related papers  (2020-05-27T12:16:24Z)

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.