GS2POSE: Marry Gaussian Splatting to 6D Object Pose Estimation

• URL: http://arxiv.org/abs/2510.16777v1
• Date: Sun, 19 Oct 2025 10:02:42 GMT
• Title: GS2POSE: Marry Gaussian Splatting to 6D Object Pose Estimation
• Authors: Junbo Li, Weimin Yuan, Yinuo Wang, Yue Zeng, Shihao Shu, Cai Meng, Xiangzhi Bai,
• Abstract summary: We propose GS2POSE, a novel approach for 6D object pose estimation.<n> GS2POSE formulates a pose regression algorithm inspired by the principles of Bundle Adjustment (BA)<n>We show that GS2POSE demonstrates accuracy improvements of 1.4%, 2.8% and 2.5% on the T-LESS, LineMod-Occlusion and LineMod datasets, respectively.
• Score: 12.402238708921493
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate 6D pose estimation of 3D objects is a fundamental task in computer vision, and current research typically predicts the 6D pose by establishing correspondences between 2D image features and 3D model features. However, these methods often face difficulties with textureless objects and varying illumination conditions. To overcome these limitations, we propose GS2POSE, a novel approach for 6D object pose estimation. GS2POSE formulates a pose regression algorithm inspired by the principles of Bundle Adjustment (BA). By leveraging Lie algebra, we extend the capabilities of 3DGS to develop a pose-differentiable rendering pipeline, which iteratively optimizes the pose by comparing the input image to the rendered image. Additionally, GS2POSE updates color parameters within the 3DGS model, enhancing its adaptability to changes in illumination. Compared to previous models, GS2POSE demonstrates accuracy improvements of 1.4\%, 2.8\% and 2.5\% on the T-LESS, LineMod-Occlusion and LineMod datasets, respectively.

Related papers

• Any6D: Model-free 6D Pose Estimation of Novel Objects [76.30057578269668]
  We introduce Any6D, a model-free framework for 6D object pose estimation.<n>It requires only a single RGB-D anchor image to estimate both the 6D pose and size of unknown objects in novel scenes.<n>We evaluate our method on five challenging datasets.
  arXiv  Detail & Related papers  (2025-03-24T13:46:21Z)
• 6DOPE-GS: Online 6D Object Pose Estimation using Gaussian Splatting [7.7145084897748974]
  We present 6DOPE-GS, a novel method for online 6D object pose estimation & tracking with a single RGB-D camera.<n>We show that 6DOPE-GS matches the performance of state-of-the-art baselines for model-free simultaneous 6D pose tracking and reconstruction.<n>We also demonstrate the method's suitability for live, dynamic object tracking and reconstruction in a real-world setting.
  arXiv  Detail & Related papers  (2024-12-02T14:32:19Z)
• GS2Pose: Two-stage 6D Object Pose Estimation Guided by Gaussian Splatting [4.465134753953128]
  This paper proposes a new method for accurate and robust 6D pose estimation of novel objects, named GS2Pose. By introducing 3D Gaussian splatting, GS2Pose can utilize the reconstruction results without requiring a high-quality CAD model. The code for GS2Pose will soon be released on GitHub.
  arXiv  Detail & Related papers  (2024-11-06T10:07:46Z)
• UPose3D: Uncertainty-Aware 3D Human Pose Estimation with Cross-View and Temporal Cues [55.69339788566899]
  UPose3D is a novel approach for multi-view 3D human pose estimation. It improves robustness and flexibility without requiring direct 3D annotations.
  arXiv  Detail & Related papers  (2024-04-23T00:18:00Z)
• 3D Neural Embedding Likelihood: Probabilistic Inverse Graphics for Robust 6D Pose Estimation [50.15926681475939]
  Inverse graphics aims to infer the 3D scene structure from 2D images. We introduce probabilistic modeling to quantify uncertainty and achieve robustness in 6D pose estimation tasks. 3DNEL effectively combines learned neural embeddings from RGB with depth information to improve robustness in sim-to-real 6D object pose estimation from RGB-D images.
  arXiv  Detail & Related papers  (2023-02-07T20:48:35Z)
• Coupled Iterative Refinement for 6D Multi-Object Pose Estimation [64.7198752089041]
  Given a set of known 3D objects and an RGB or RGB-D input image, we detect and estimate the 6D pose of each object. Our approach iteratively refines both pose and correspondence in a tightly coupled manner, allowing us to dynamically remove outliers to improve accuracy.
  arXiv  Detail & Related papers  (2022-04-26T18:00:08Z)
• SO-Pose: Exploiting Self-Occlusion for Direct 6D Pose Estimation [98.83762558394345]
  SO-Pose is a framework for regressing all 6 degrees-of-freedom (6DoF) for the object pose in a cluttered environment from a single RGB image. We introduce a novel reasoning about self-occlusion, in order to establish a two-layer representation for 3D objects. Cross-layer consistencies that align correspondences, self-occlusion and 6D pose, we can further improve accuracy and robustness.
  arXiv  Detail & Related papers  (2021-08-18T19:49:29Z)
• GDRNPP: A Geometry-guided and Fully Learning-based Object Pose Estimator [51.89441403642665]
  6D pose estimation of rigid objects is a long-standing and challenging task in computer vision.<n>Recently, the emergence of deep learning reveals the potential of Convolutional Neural Networks (CNNs) to predict reliable 6D poses.<n>This paper introduces a fully learning-based object pose estimator.
  arXiv  Detail & Related papers  (2021-02-24T09:11:31Z)

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.