DQO-MAP: Dual Quadrics Multi-Object mapping with Gaussian Splatting

• URL: http://arxiv.org/abs/2503.02223v1
• Date: Tue, 04 Mar 2025 02:55:07 GMT
• Title: DQO-MAP: Dual Quadrics Multi-Object mapping with Gaussian Splatting
• Authors: Haoyuan Li, Ziqin Ye, Yue Hao, Weiyang Lin, Chao Ye,
• Abstract summary: We propose a novel object-SLAM system that seamlessly integrates object pose estimation and reconstruction.<n>DQO-MAP achieves outstanding performance in terms of precision, reconstruction quality, and computational efficiency.
• Score: 6.736949053673975
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate object perception is essential for robotic applications such as object navigation. In this paper, we propose DQO-MAP, a novel object-SLAM system that seamlessly integrates object pose estimation and reconstruction. We employ 3D Gaussian Splatting for high-fidelity object reconstruction and leverage quadrics for precise object pose estimation. Both of them management is handled on the CPU, while optimization is performed on the GPU, significantly improving system efficiency. By associating objects with unique IDs, our system enables rapid object extraction from the scene. Extensive experimental results on object reconstruction and pose estimation demonstrate that DQO-MAP achieves outstanding performance in terms of precision, reconstruction quality, and computational efficiency. The code and dataset are available at: https://github.com/LiHaoy-ux/DQO-MAP.

Related papers

• Building Interactable Replicas of Complex Articulated Objects via Gaussian Splatting [66.29782808719301]
  Building articulated objects is a key challenge in computer vision.<n>Existing methods often fail to effectively integrate information across different object states.<n>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)
• GIC: Gaussian-Informed Continuum for Physical Property Identification and Simulation [60.33467489955188]
  This paper studies the problem of estimating physical properties (system identification) through visual observations. To facilitate geometry-aware guidance in physical property estimation, we introduce a novel hybrid framework. We propose a new dynamic 3D Gaussian framework based on motion factorization to recover the object as 3D Gaussian point sets. In addition to the extracted object surfaces, the Gaussian-informed continuum also enables the rendering of object masks during simulations.
  arXiv  Detail & Related papers  (2024-06-21T07:37:17Z)
• VOOM: Robust Visual Object Odometry and Mapping using Hierarchical Landmarks [19.789761641342043]
  We propose a Visual Object Odometry and Mapping framework VOOM. We use high-level objects and low-level points as the hierarchical landmarks in a coarse-to-fine manner. VOOM outperforms both object-oriented SLAM and feature points SLAM systems in terms of localization.
  arXiv  Detail & Related papers  (2024-02-21T08:22:46Z)
• Uncertainty-aware 3D Object-Level Mapping with Deep Shape Priors [15.34487368683311]
  We propose a framework that can reconstruct high-quality object-level maps for unknown objects. Our approach takes multiple RGB-D images as input and outputs dense 3D shapes and 9-DoF poses for detected objects. We derive a probabilistic formulation that propagates shape and pose uncertainty through two novel loss functions.
  arXiv  Detail & Related papers  (2023-09-17T00:48:19Z)
• 3D Video Object Detection with Learnable Object-Centric Global Optimization [65.68977894460222]
  Correspondence-based optimization is the cornerstone for 3D scene reconstruction but is less studied in 3D video object detection. We propose BA-Det, an end-to-end optimizable object detector with object-centric temporal correspondence learning and featuremetric object bundle adjustment.
  arXiv  Detail & Related papers  (2023-03-27T17:39:39Z)
• Efficient 3D Deep LiDAR Odometry [16.388259779644553]
  An efficient 3D point cloud learning architecture, named PWCLO-Net, is first proposed in this paper. The entire architecture is holistically optimized end-to-end to achieve adaptive learning of cost volume and mask.
  arXiv  Detail & Related papers  (2021-11-03T11:09:49Z)
• Object-Augmented RGB-D SLAM for Wide-Disparity Relocalisation [3.888848425698769]
  We propose a novel object-augmented RGB-D SLAM system that is capable of constructing a consistent object map and performing relocalisation based on centroids of objects in the map.
  arXiv  Detail & Related papers  (2021-08-05T11:02:25Z)
• Analysis of voxel-based 3D object detection methods efficiency for real-time embedded systems [93.73198973454944]
  Two popular voxel-based 3D object detection methods are studied in this paper. Our experiments show that these methods mostly fail to detect distant small objects due to the sparsity of the input point clouds at large distances. Our findings suggest that a considerable part of the computations of existing methods is focused on locations of the scene that do not contribute with successful detection.
  arXiv  Detail & Related papers  (2021-05-21T12:40:59Z)
• Objects are Different: Flexible Monocular 3D Object Detection [87.82253067302561]
  We propose a flexible framework for monocular 3D object detection which explicitly decouples the truncated objects and adaptively combines multiple approaches for object depth estimation. Experiments demonstrate that our method outperforms the state-of-the-art method by relatively 27% for the moderate level and 30% for the hard level in the test set of KITTI benchmark.
  arXiv  Detail & Related papers  (2021-04-06T07:01:28Z)
• 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)
• OrcVIO: Object residual constrained Visual-Inertial Odometry [18.3130718336919]
  This work presents OrcVIO, for visual-inertial odometry tightly coupled with tracking and optimization over structured object models. The ability of OrcVIO for accurate trajectory estimation and large-scale object-level mapping is evaluated using real data.
  arXiv  Detail & Related papers  (2020-07-29T21:01:37Z)

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.