Analysis and Prediction of Deforming 3D Shapes using Oriented Bounding Boxes and LSTM Autoencoders

• URL: http://arxiv.org/abs/2009.03782v1
• Date: Mon, 31 Aug 2020 08:07:32 GMT
• Title: Analysis and Prediction of Deforming 3D Shapes using Oriented Bounding Boxes and LSTM Autoencoders
• Authors: Sara Hahner, Rodrigo Iza-Teran, Jochen Garcke
• Abstract summary: The architecture is tested on the results of 196 car crash simulations of a model with 133 different components. In the latent representation we can detect patterns in the plastic deformation for the different components. The predicted bounding boxes give an estimate of the final simulation result and their quality is improved in comparison to different baselines.
• Score: 0.966840768820136
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: For sequences of complex 3D shapes in time we present a general approach to detect patterns for their analysis and to predict the deformation by making use of structural components of the complex shape. We incorporate long short-term memory (LSTM) layers into an autoencoder to create low dimensional representations that allow the detection of patterns in the data and additionally detect the temporal dynamics in the deformation behavior. This is achieved with two decoders, one for reconstruction and one for prediction of future time steps of the sequence. In a preprocessing step the components of the studied object are converted to oriented bounding boxes which capture the impact of plastic deformation and allow reducing the dimensionality of the data describing the structure. The architecture is tested on the results of 196 car crash simulations of a model with 133 different components, where material properties are varied. In the latent representation we can detect patterns in the plastic deformation for the different components. The predicted bounding boxes give an estimate of the final simulation result and their quality is improved in comparison to different baselines.

Related papers

• ReshapeIT: Reliable Shape Interaction with Implicit Template for Anatomical Structure Reconstruction [59.971808117043366]
  ReShapeIT represents an anatomical structure with an implicit template field shared within the same category. It ensures the implicit template field generates valid templates by strengthening the constraint of the correspondence between the instance shape and the template shape. A template Interaction Module is introduced to reconstruct unseen shapes by interacting the valid template shapes with the instance-wise latent codes.
  arXiv  Detail & Related papers  (2023-12-11T07:09:32Z)
• Explorable Mesh Deformation Subspaces from Unstructured Generative Models [53.23510438769862]
  Deep generative models of 3D shapes often feature continuous latent spaces that can be used to explore potential variations. We present a method to explore variations among a given set of landmark shapes by constructing a mapping from an easily-navigable 2D exploration space to a subspace of a pre-trained generative model.
  arXiv  Detail & Related papers  (2023-10-11T18:53:57Z)
• DTF-Net: Category-Level Pose Estimation and Shape Reconstruction via Deformable Template Field [29.42222066097076]
  Estimating 6D poses and reconstructing 3D shapes of objects in open-world scenes from RGB-depth image pairs is challenging. We propose the DTF-Net, a novel framework for pose estimation and shape reconstruction based on implicit neural fields of object categories.
  arXiv  Detail & Related papers  (2023-08-04T10:35:40Z)
• Neural Template: Topology-aware Reconstruction and Disentangled Generation of 3D Meshes [52.038346313823524]
  This paper introduces a novel framework called DTNet for 3D mesh reconstruction and generation via Disentangled Topology. Our method is able to produce high-quality meshes, particularly with diverse topologies, as compared with the state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-06-10T08:32:57Z)
• Pixel2Mesh++: 3D Mesh Generation and Refinement from Multi-View Images [82.32776379815712]
  We study the problem of shape generation in 3D mesh representation from a small number of color images with or without camera poses. We adopt to further improve the shape quality by leveraging cross-view information with a graph convolution network. Our model is robust to the quality of the initial mesh and the error of camera pose, and can be combined with a differentiable function for test-time optimization.
  arXiv  Detail & Related papers  (2022-04-21T03:42:31Z)
• SPAMs: Structured Implicit Parametric Models [30.19414242608965]
  We learn Structured-implicit PArametric Models (SPAMs) as a deformable object representation that structurally decomposes non-rigid object motion into part-based disentangled representations of shape and pose. Experiments demonstrate that our part-aware shape and pose understanding lead to state-of-the-art performance in reconstruction and tracking of depth sequences of complex deforming object motion.
  arXiv  Detail & Related papers  (2022-01-20T12:33:46Z)
• Disentangling Geometric Deformation Spaces in Generative Latent Shape Models [5.582957809895198]
  A complete representation of 3D objects requires characterizing the space of deformations in an interpretable manner. We improve on a prior generative model of disentanglement for 3D shapes, wherein the space of object geometry is factorized into rigid orientation, non-rigid pose, and intrinsic shape. The resulting model can be trained from raw 3D shapes, without correspondences, labels, or even rigid alignment.
  arXiv  Detail & Related papers  (2021-02-27T06:54:31Z)
• Category Level Object Pose Estimation via Neural Analysis-by-Synthesis [64.14028598360741]
  In this paper we combine a gradient-based fitting procedure with a parametric neural image synthesis module. The image synthesis network is designed to efficiently span the pose configuration space. We experimentally show that the method can recover orientation of objects with high accuracy from 2D images alone.
  arXiv  Detail & Related papers  (2020-08-18T20:30:47Z)
• Dense Non-Rigid Structure from Motion: A Manifold Viewpoint [162.88686222340962]
  Non-Rigid Structure-from-Motion (NRSfM) problem aims to recover 3D geometry of a deforming object from its 2D feature correspondences across multiple frames. We show that our approach significantly improves accuracy, scalability, and robustness against noise.
  arXiv  Detail & Related papers  (2020-06-15T09:15:54Z)
• A Compact Spectral Descriptor for Shape Deformations [0.8268443804509721]
  We propose a novel methodology to obtain a parameterization of a component's plastic deformation behavior under stress. Existing parameterizations limit computational analysis to relatively simple deformations. We propose a way to derive a compact descriptor of deformation behavior based on spectral mesh processing.
  arXiv  Detail & Related papers  (2020-03-10T10:34:30Z)

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.