Category-level Neural Field for Reconstruction of Partially Observed Objects in Indoor Environment

• URL: http://arxiv.org/abs/2406.08176v1
• Date: Wed, 12 Jun 2024 13:09:59 GMT
• Title: Category-level Neural Field for Reconstruction of Partially Observed Objects in Indoor Environment
• Authors: Taekbeom Lee, Youngseok Jang, H. Jin Kim,
• Abstract summary: We introduce category-level neural fields that learn meaningful common 3D information among objects belonging to the same category present in the scene. Our key idea is to subcategorize objects based on their observed shape for better training of the category-level model. Experiments on both simulation and real-world datasets demonstrate that our method improves the reconstruction of unobserved parts for several categories.
• Score: 24.880495520422006
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Neural implicit representation has attracted attention in 3D reconstruction through various success cases. For further applications such as scene understanding or editing, several works have shown progress towards object compositional reconstruction. Despite their superior performance in observed regions, their performance is still limited in reconstructing objects that are partially observed. To better treat this problem, we introduce category-level neural fields that learn meaningful common 3D information among objects belonging to the same category present in the scene. Our key idea is to subcategorize objects based on their observed shape for better training of the category-level model. Then we take advantage of the neural field to conduct the challenging task of registering partially observed objects by selecting and aligning against representative objects selected by ray-based uncertainty. Experiments on both simulation and real-world datasets demonstrate that our method improves the reconstruction of unobserved parts for several categories.

Related papers

• Detection Based Part-level Articulated Object Reconstruction from Single RGBD Image [52.11275397911693]
  We propose an end-to-end trainable, cross-category method for reconstructing multiple man-made articulated objects from a single RGBD image. We depart from previous works that rely on learning instance-level latent space, focusing on man-made articulated objects with predefined part counts. Our method successfully reconstructs variously structured multiple instances that previous works cannot handle, and outperforms prior works in shape reconstruction and kinematics estimation.
  arXiv  Detail & Related papers  (2025-04-04T05:08:04Z)
• Online 3D Scene Reconstruction Using Neural Object Priors [83.14204014687938]
  This paper addresses the problem of reconstructing a scene online at the level of objects given an RGB-D video sequence. We propose a feature grid mechanism to continuously update object-centric neural implicit representations as new object parts are revealed. Our approach outperforms state-of-the-art neural implicit models for this task in terms of reconstruction accuracy and completeness.
  arXiv  Detail & Related papers  (2025-03-24T17:09:36Z)
• ArtGS: Building Interactable Replicas of Complex Articulated Objects via Gaussian Splatting [66.29782808719301]
  Building articulated objects is a key challenge in computer vision. Existing methods often fail to effectively integrate information across different object states. We introduce ArtGS, a novel approach that leverages 3D Gaussians as a flexible and efficient representation.
  arXiv  Detail & Related papers  (2025-02-26T10:25:32Z)
• Zero-Shot Object-Centric Representation Learning [72.43369950684057]
  We study current object-centric methods through the lens of zero-shot generalization. We introduce a benchmark comprising eight different synthetic and real-world datasets. We find that training on diverse real-world images improves transferability to unseen scenarios.
  arXiv  Detail & Related papers  (2024-08-17T10:37:07Z)
• SimNP: Learning Self-Similarity Priors Between Neural Points [52.4201466988562]
  SimNP is a method to learn category-level self-similarities. We show that SimNP is able to outperform previous methods in reconstructing symmetric unseen object regions.
  arXiv  Detail & Related papers  (2023-09-07T16:02:40Z)
• Single-view 3D Mesh Reconstruction for Seen and Unseen Categories [69.29406107513621]
  Single-view 3D Mesh Reconstruction is a fundamental computer vision task that aims at recovering 3D shapes from single-view RGB images. This paper tackles Single-view 3D Mesh Reconstruction, to study the model generalization on unseen categories. We propose an end-to-end two-stage network, GenMesh, to break the category boundaries in reconstruction.
  arXiv  Detail & Related papers  (2022-08-04T14:13:35Z)
• Visual Ground Truth Construction as Faceted Classification [4.7590051176368915]
  Key novelty of our approach lies in the fact that we construct the classification hierarchies from visual properties exploiting visual genus-differentiae. The proposed approach is validated by a set of experiments on the ImageNet hierarchy of musical experiments.
  arXiv  Detail & Related papers  (2022-02-17T08:35:23Z)
• Unsupervised Part Discovery from Contrastive Reconstruction [90.88501867321573]
  The goal of self-supervised visual representation learning is to learn strong, transferable image representations. We propose an unsupervised approach to object part discovery and segmentation. Our method yields semantic parts consistent across fine-grained but visually distinct categories.
  arXiv  Detail & Related papers  (2021-11-11T17:59:42Z)
• StrobeNet: Category-Level Multiview Reconstruction of Articulated Objects [17.698319441265223]
  StrobeNet is a method for category-level 3D reconstruction of articulating objects from unposed RGB images. Our approach reconstructs objects even when they are observed in different articulations in images with large baselines.
  arXiv  Detail & Related papers  (2021-05-17T17:05:42Z)
• Unsupervised Learning of 3D Object Categories from Videos in the Wild [75.09720013151247]
  We focus on learning a model from multiple views of a large collection of object instances. We propose a new neural network design, called warp-conditioned ray embedding (WCR), which significantly improves reconstruction. Our evaluation demonstrates performance improvements over several deep monocular reconstruction baselines on existing benchmarks.
  arXiv  Detail & Related papers  (2021-03-30T17:57:01Z)
• On the generalization of learning-based 3D reconstruction [10.516860541554632]
  We study the inductive biases encoded in the model architecture that impact the generalization of learning-based 3D reconstruction methods. We find that 3 inductive biases impact performance: the spatial extent of the encoder, the use of the underlying geometry of the scene to describe point features, and the mechanism to aggregate information from multiple views.
  arXiv  Detail & Related papers  (2020-06-27T18:53:41Z)
• Fine-Grained 3D Shape Classification with Hierarchical Part-View Attentions [70.0171362989609]
  We propose a novel fine-grained 3D shape classification method named FG3D-Net to capture the fine-grained local details of 3D shapes from multiple rendered views. Our results under the fine-grained 3D shape dataset show that our method outperforms other state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-05-26T06:53:19Z)

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.