Reconstructing Interactive 3D Scenes by Panoptic Mapping and CAD Model Alignments

• URL: http://arxiv.org/abs/2103.16095v1
• Date: Tue, 30 Mar 2021 05:56:58 GMT
• Title: Reconstructing Interactive 3D Scenes by Panoptic Mapping and CAD Model Alignments
• Authors: Muzhi Han, Zeyu Zhang, Ziyuan Jiao, Xu Xie, Yixin Zhu, Song-Chun Zhu, Hangxin Liu
• Abstract summary: We rethink the problem of scene reconstruction from an embodied agent's perspective. We reconstruct an interactive scene using RGB-D data stream. This reconstructed scene replaces the object meshes in the dense panoptic map with part-based articulated CAD models.
• Score: 81.38641691636847
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we rethink the problem of scene reconstruction from an embodied agent's perspective: While the classic view focuses on the reconstruction accuracy, our new perspective emphasizes the underlying functions and constraints such that the reconstructed scenes provide \em{actionable} information for simulating \em{interactions} with agents. Here, we address this challenging problem by reconstructing an interactive scene using RGB-D data stream, which captures (i) the semantics and geometry of objects and layouts by a 3D volumetric panoptic mapping module, and (ii) object affordance and contextual relations by reasoning over physical common sense among objects, organized by a graph-based scene representation. Crucially, this reconstructed scene replaces the object meshes in the dense panoptic map with part-based articulated CAD models for finer-grained robot interactions. In the experiments, we demonstrate that (i) our panoptic mapping module outperforms previous state-of-the-art methods, (ii) a high-performant physical reasoning procedure that matches, aligns, and replaces objects' meshes with best-fitted CAD models, and (iii) reconstructed scenes are physically plausible and naturally afford actionable interactions; without any manual labeling, they are seamlessly imported to ROS-based simulators and virtual environments for complex robot task executions.

Related papers

• CAST: Component-Aligned 3D Scene Reconstruction from an RGB Image [44.8172828045897]
  Current methods often struggle with domain-specific limitations or low-quality object generation. We propose CAST, a novel method for 3D scene reconstruction and recovery.
  arXiv  Detail & Related papers  (2025-02-18T14:29:52Z)
• Diorama: Unleashing Zero-shot Single-view 3D Scene Modeling [27.577720075952225]
  We present Diorama, the first zero-shot open-world system that holistically models 3D scenes from single-view RGB observations. We show the feasibility of our approach by decomposing the problem into subtasks and introduce robust, generalizable solutions to each.
  arXiv  Detail & Related papers  (2024-11-29T06:19:04Z)
• Dynamic Scene Understanding through Object-Centric Voxelization and Neural Rendering [57.895846642868904]
  We present a 3D generative model named DynaVol-S for dynamic scenes that enables object-centric learning. voxelization infers per-object occupancy probabilities at individual spatial locations. Our approach integrates 2D semantic features to create 3D semantic grids, representing the scene through multiple disentangled voxel grids.
  arXiv  Detail & Related papers  (2024-07-30T15:33:58Z)
• MaRINeR: Enhancing Novel Views by Matching Rendered Images with Nearby References [49.71130133080821]
  MaRINeR is a refinement method that leverages information of a nearby mapping image to improve the rendering of a target viewpoint. We show improved renderings in quantitative metrics and qualitative examples from both explicit and implicit scene representations.
  arXiv  Detail & Related papers  (2024-07-18T17:50:03Z)
• SMORE: Simulataneous Map and Object REconstruction [66.66729715211642]
  We present a method for dynamic surface reconstruction of large-scale urban scenes from LiDAR. We take a holistic perspective and optimize a compositional model of a dynamic scene that decomposes the world into rigidly-moving objects and the background.
  arXiv  Detail & Related papers  (2024-06-19T23:53:31Z)
• Total-Decom: Decomposed 3D Scene Reconstruction with Minimal Interaction [51.3632308129838]
  We present Total-Decom, a novel method for decomposed 3D reconstruction with minimal human interaction. Our approach seamlessly integrates the Segment Anything Model (SAM) with hybrid implicit-explicit neural surface representations and a mesh-based region-growing technique for accurate 3D object decomposition. We extensively evaluate our method on benchmark datasets and demonstrate its potential for downstream applications, such as animation and scene editing.
  arXiv  Detail & Related papers  (2024-03-28T11:12:33Z)
• Interaction-Driven Active 3D Reconstruction with Object Interiors [17.48872400701787]
  We introduce an active 3D reconstruction method which integrates visual perception, robot-object interaction, and 3D scanning. Our method operates fully automatically by a Fetch robot with built-in RGBD sensors.
  arXiv  Detail & Related papers  (2023-10-23T08:44:38Z)
• 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)
• Dynamic Object Removal and Spatio-Temporal RGB-D Inpainting via Geometry-Aware Adversarial Learning [9.150245363036165]
  Dynamic objects have a significant impact on the robot's perception of the environment. In this work, we address this problem by synthesizing plausible color, texture and geometry in regions occluded by dynamic objects. We optimize our architecture using adversarial training to synthesize fine realistic textures which enables it to hallucinate color and depth structure in occluded regions online.
  arXiv  Detail & Related papers  (2020-08-12T01:23:21Z)

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.