Rad-GS: Radar-Vision Integration for 3D Gaussian Splatting SLAM in Outdoor Environments

• URL: http://arxiv.org/abs/2511.16091v1
• Date: Thu, 20 Nov 2025 06:32:46 GMT
• Title: Rad-GS: Radar-Vision Integration for 3D Gaussian Splatting SLAM in Outdoor Environments
• Authors: Renxiang Xiao, Wei Liu, Yuanfan Zhang, Yushuai Chen, Jinming Chen, Zilu Wang, Liang Hu,
• Abstract summary: Rad-GS is a 4D radar-camera SLAM system designed for kilometer-scale outdoor environments.<n>We show that Rad-GS achieves performance comparable to traditional 3D Gaussian methods based on camera or LiDAR inputs.
• Score: 7.035816744210293
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We present Rad-GS, a 4D radar-camera SLAM system designed for kilometer-scale outdoor environments, utilizing 3D Gaussian as a differentiable spatial representation. Rad-GS combines the advantages of raw radar point cloud with Doppler information and geometrically enhanced point cloud to guide dynamic object masking in synchronized images, thereby alleviating rendering artifacts and improving localization accuracy. Additionally, unsynchronized image frames are leveraged to globally refine the 3D Gaussian representation, enhancing texture consistency and novel view synthesis fidelity. Furthermore, the global octree structure coupled with a targeted Gaussian primitive management strategy further suppresses noise and significantly reduces memory consumption in large-scale environments. Extensive experiments and ablation studies demonstrate that Rad-GS achieves performance comparable to traditional 3D Gaussian methods based on camera or LiDAR inputs, highlighting the feasibility of robust outdoor mapping using 4D mmWave radar. Real-world reconstruction at kilometer scale validates the potential of Rad-GS for large-scale scene reconstruction.

Related papers

• 3D Scene Rendering with Multimodal Gaussian Splatting [7.646123713331197]
  3D scene reconstruction and rendering are core tasks in computer vision, with applications spanning industrial monitoring, robotics, and autonomous driving.<n>Recent advances in 3D Gaussian Splatting have achieved impressive rendering fidelity while maintaining high computational and memory efficiency.<n>We introduce a multimodal framework that integrates RF sensing, such as automotive radar, with GS-based rendering as a more efficient and robust alternative to vision-only GS rendering.
  arXiv  Detail & Related papers  (2026-02-19T06:49:53Z)
• Improved 3D Gaussian Splatting of Unknown Spacecraft Structure Using Space Environment Illumination Knowledge [7.2620484413601325]
  This work presents a novel pipeline to recover the 3D structure of an unknown target spacecraft from a sequence of images captured in space.<n>Learning 3DGS requires static scenes, an assumption in contrast to dynamic lighting conditions encountered in spaceborne imagery.<n>The trained 3DGS model can also be used for camera pose estimation through photometric optimization.
  arXiv  Detail & Related papers  (2025-12-30T05:35:35Z)
• SING3R-SLAM: Submap-based Indoor Monocular Gaussian SLAM with 3D Reconstruction Priors [80.51557267896938]
  SING3R-SLAM is a globally consistent and compact Gaussian-based dense RGB SLAM framework.<n>We show that SING3R-SLAM achieves state-of-the-art tracking, 3D reconstruction, and novel view rendering, resulting in over 12% improvement in tracking and producing finer, more detailed geometry.
  arXiv  Detail & Related papers  (2025-11-21T12:40:55Z)
• RaGS: Unleashing 3D Gaussian Splatting from 4D Radar and Monocular Cues for 3D Object Detection [22.546559563539272]
  We propose RaGS, a framework that leverages 3D Gaussian Splatting to fuse 4D radar and monocular cues for 3D object detection.<n>RaGS achieves object-centric precision and comprehensive scene perception.
  arXiv  Detail & Related papers  (2025-07-26T08:17:12Z)
• EVolSplat: Efficient Volume-based Gaussian Splatting for Urban View Synthesis [61.1662426227688]
  Existing NeRF and 3DGS-based methods show promising results in achieving photorealistic renderings but require slow, per-scene optimization.<n>We introduce EVolSplat, an efficient 3D Gaussian Splatting model for urban scenes that works in a feed-forward manner.
  arXiv  Detail & Related papers  (2025-03-26T02:47:27Z)
• LiDAR-GS:Real-time LiDAR Re-Simulation using Gaussian Splatting [53.58528891081709]
  We present LiDAR-GS, a real-time, high-fidelity re-simulation of LiDAR scans in public urban road scenes.<n>The method achieves state-of-the-art results in both rendering frame rate and quality on publically available large scene datasets.
  arXiv  Detail & Related papers  (2024-10-07T15:07:56Z)
• Optimizing 3D Gaussian Splatting for Sparse Viewpoint Scene Reconstruction [11.840097269724792]
  3D Gaussian Splatting (3DGS) has emerged as a promising approach for 3D scene representation, offering a reduction in computational overhead compared to Neural Radiance Fields (NeRF) We introduce SVS-GS, a novel framework for Sparse Viewpoint Scene reconstruction that integrates a 3D Gaussian smoothing filter to suppress artifacts.
  arXiv  Detail & Related papers  (2024-09-05T03:18:04Z)
• PUP 3D-GS: Principled Uncertainty Pruning for 3D Gaussian Splatting [59.277480452459315]
  We propose a principled sensitivity pruning score that preserves visual fidelity and foreground details at significantly higher compression ratios.<n>We also propose a multi-round prune-refine pipeline that can be applied to any pretrained 3D-GS model without changing its training pipeline.
  arXiv  Detail & Related papers  (2024-06-14T17:53:55Z)
• MM3DGS SLAM: Multi-modal 3D Gaussian Splatting for SLAM Using Vision, Depth, and Inertial Measurements [59.70107451308687]
  We show for the first time that using 3D Gaussians for map representation with unposed camera images and inertial measurements can enable accurate SLAM. Our method, MM3DGS, addresses the limitations of prior rendering by enabling faster scale awareness, and improved trajectory tracking. We also release a multi-modal dataset, UT-MM, collected from a mobile robot equipped with a camera and an inertial measurement unit.
  arXiv  Detail & Related papers  (2024-04-01T04:57:41Z)
• Gaussian Splatting SLAM [16.3858380078553]
  We present the first application of 3D Gaussian Splatting in monocular SLAM. Our method runs live at 3fps, unifying the required representation for accurate tracking, mapping, and high-quality rendering. Several innovations are required to continuously reconstruct 3D scenes with high fidelity from a live camera.
  arXiv  Detail & Related papers  (2023-12-11T18:19:04Z)
• LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPS [55.85673901231235]
  We introduce LightGaussian, a method for transforming 3D Gaussians into a more compact format. Inspired by Network Pruning, LightGaussian identifies Gaussians with minimal global significance on scene reconstruction. LightGaussian achieves an average 15x compression rate while boosting FPS from 144 to 237 within the 3D-GS framework.
  arXiv  Detail & Related papers  (2023-11-28T21:39:20Z)

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.