Gaussian Semantic Field for One-shot LiDAR Global Localization

• URL: http://arxiv.org/abs/2510.12101v1
• Date: Tue, 14 Oct 2025 03:08:02 GMT
• Title: Gaussian Semantic Field for One-shot LiDAR Global Localization
• Authors: Pengyu Yin, Shenghai Yuan, Haozhi Cao, Xingyu Ji, Ruofei Bai, Siyu Chen, Lihua Xie,
• Abstract summary: We present a one-shot LiDAR global localization algorithm featuring semantic disambiguation ability based on a tri-layered scene graph.<n>We propose to mitigate this problem by modeling semantic distributions with continuous functions learned from a population of Gaussian processes.<n>Compared with discrete semantic labels, the continuous functions capture finer-grained geo-semantic information and also provide more detailed metric information for correspondence establishment.
• Score: 39.349352888906466
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a one-shot LiDAR global localization algorithm featuring semantic disambiguation ability based on a lightweight tri-layered scene graph. While landmark semantic registration-based methods have shown promising performance improvements in global localization compared with geometric-only methods, landmarks can be repetitive and misleading for correspondence establishment. We propose to mitigate this problem by modeling semantic distributions with continuous functions learned from a population of Gaussian processes. Compared with discrete semantic labels, the continuous functions capture finer-grained geo-semantic information and also provide more detailed metric information for correspondence establishment. We insert this continuous function as the middle layer between the object layer and the metric-semantic layer, forming a tri-layered 3D scene graph, serving as a light-weight yet performant backend for one-shot localization. We term our global localization pipeline Outram-GSF (Gaussian semantic field) and conduct a wide range of experiments on publicly available data sets, validating the superior performance against the current state-of-the-art.

Related papers

• Joint Semantic and Rendering Enhancements in 3D Gaussian Modeling with Anisotropic Local Encoding [86.55824709875598]
  We propose a joint enhancement framework for 3D semantic Gaussian modeling that synergizes both semantic and rendering branches.<n>Unlike conventional point cloud shape encoding, we introduce an anisotropic 3D Gaussian Chebyshev descriptor to capture fine-grained 3D shape details.<n>We employ a cross-scene knowledge transfer module to continuously update learned shape patterns, enabling faster convergence and robust representations.
  arXiv  Detail & Related papers  (2026-01-05T18:33:50Z)
• Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition [63.55828203989405]
  We introduce a novel Topology-Aware Modeling (TAM) framework for Sim2Real UDA on object point clouds.<n>Our approach mitigates the domain gap by leveraging global spatial topology, characterized by low-level, high-frequency 3D structures.<n>We propose an advanced self-training strategy that combines cross-domain contrastive learning with self-training.
  arXiv  Detail & Related papers  (2025-06-26T11:53:59Z)
• TrajDiff: Diffusion Bridge Network with Semantic Alignment for Trajectory Similarity Computation [39.42796455901991]
  We propose a novel trajectory similarity computation framework, named TrajDiff.<n>Specifically, TrajDiff relies on cross-attention and an attention score mask mechanism with adaptive fusion.<n>Extensive experiments on three publicly available datasets show that TrajDiff consistently outperforms state-of-the-art baselines.
  arXiv  Detail & Related papers  (2025-06-18T21:52:07Z)
• STAMICS: Splat, Track And Map with Integrated Consistency and Semantics for Dense RGB-D SLAM [8.208389210258593]
  We introduce STAMICS, a novel method that integrates semantic information with 3D Gaussian representations to enhance both localization and mapping accuracy.<n>Experiments show that STAMICS significantly improves camera pose estimation and map quality, outperforming state-of-the-art methods while reducing reconstruction errors.
  arXiv  Detail & Related papers  (2025-03-27T12:10:51Z)
• SemanticTopoLoop: Semantic Loop Closure With 3D Topological Graph Based on Quadric-Level Object Map [0.8158530638728501]
  Loop closure is one of the crucial components in SLAM. Traditional appearance-based methods, such as bag-of-words models, are often limited by local 2D features and the volume of training data.
  arXiv  Detail & Related papers  (2023-11-06T02:30:30Z)
• Volumetric Semantically Consistent 3D Panoptic Mapping [77.13446499924977]
  We introduce an online 2D-to-3D semantic instance mapping algorithm aimed at generating semantic 3D maps suitable for autonomous agents in unstructured environments. It introduces novel ways of integrating semantic prediction confidence during mapping, producing semantic and instance-consistent 3D regions. The proposed method achieves accuracy superior to the state of the art on public large-scale datasets, improving on a number of widely used metrics.
  arXiv  Detail & Related papers  (2023-09-26T08:03:10Z)
• Single-Shot Global Localization via Graph-Theoretic Correspondence Matching [16.956872056232633]
  The proposed framework employs correspondence matching based on the maximum clique problem (MCP) We implement it with a semantically labeled 3D point cloud map, and a semantic segmentation image as a query. The method shows promising results on multiple large-scale simulated maps of urban scenes.
  arXiv  Detail & Related papers  (2023-06-06T12:52:07Z)
• Collaborative Propagation on Multiple Instance Graphs for 3D Instance Segmentation with Single-point Supervision [63.429704654271475]
  We propose a novel weakly supervised method RWSeg that only requires labeling one object with one point. With these sparse weak labels, we introduce a unified framework with two branches to propagate semantic and instance information. Specifically, we propose a Cross-graph Competing Random Walks (CRW) algorithm that encourages competition among different instance graphs.
  arXiv  Detail & Related papers  (2022-08-10T02:14:39Z)
• Spatial Pyramid Based Graph Reasoning for Semantic Segmentation [67.47159595239798]
  We apply graph convolution into the semantic segmentation task and propose an improved Laplacian. The graph reasoning is directly performed in the original feature space organized as a spatial pyramid. We achieve comparable performance with advantages in computational and memory overhead.
  arXiv  Detail & Related papers  (2020-03-23T12:28:07Z)

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.