Scaffold-SLAM: Structured 3D Gaussians for Simultaneous Localization and Photorealistic Mapping

• URL: http://arxiv.org/abs/2501.05242v2
• Date: Wed, 26 Feb 2025 07:34:39 GMT
• Title: Scaffold-SLAM: Structured 3D Gaussians for Simultaneous Localization and Photorealistic Mapping
• Authors: Tianci Wen, Zhiang Liu, Biao Lu, Yongchun Fang,
• Abstract summary: We present Scaffold-SLAM, which delivers simultaneous localization and high-quality photorealistic mapping across monocular, stereo, and RGB-D cameras.<n>First, we propose Appearance-from-Motion embedding, enabling 3D Gaussians to better model image appearance variations across different camera poses.<n>Second, we introduce a frequency regularization pyramid to guide the distribution of Gaussians, allowing the model to effectively capture finer details in the scene.
• Score: 10.876382942072933
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D Gaussian Splatting (3DGS) has recently revolutionized novel view synthesis in the Simultaneous Localization and Mapping (SLAM). However, existing SLAM methods utilizing 3DGS have failed to provide high-quality novel view rendering for monocular, stereo, and RGB-D cameras simultaneously. Notably, some methods perform well for RGB-D cameras but suffer significant degradation in rendering quality for monocular cameras. In this paper, we present Scaffold-SLAM, which delivers simultaneous localization and high-quality photorealistic mapping across monocular, stereo, and RGB-D cameras. We introduce two key innovations to achieve this state-of-the-art visual quality. First, we propose Appearance-from-Motion embedding, enabling 3D Gaussians to better model image appearance variations across different camera poses. Second, we introduce a frequency regularization pyramid to guide the distribution of Gaussians, allowing the model to effectively capture finer details in the scene. Extensive experiments on monocular, stereo, and RGB-D datasets demonstrate that Scaffold-SLAM significantly outperforms state-of-the-art methods in photorealistic mapping quality, e.g., PSNR is 16.76% higher in the TUM RGB-D datasets for monocular cameras.

Related papers

• 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. 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)
• DSplats: 3D Generation by Denoising Splats-Based Multiview Diffusion Models [67.50989119438508]
  We introduce DSplats, a novel method that directly denoises multiview images using Gaussian-based Reconstructors to produce realistic 3D assets. Our experiments demonstrate that DSplats not only produces high-quality, spatially consistent outputs, but also sets a new standard in single-image to 3D reconstruction.
  arXiv  Detail & Related papers  (2024-12-11T07:32:17Z)
• Boost 3D Reconstruction using Diffusion-based Monocular Camera Calibration [34.18403601269181]
  DM-Calib is a diffusion-based approach for estimating pinhole camera intrinsic parameters from a single input image. We introduce a new image-based representation, termed Camera Image, which losslessly encodes the numerical camera intrinsics. By fine-tuning a stable diffusion model to generate a Camera Image from a single RGB input, we can extract camera intrinsics via a RANSAC operation.
  arXiv  Detail & Related papers  (2024-11-26T09:04:37Z)
• GSLoc: Efficient Camera Pose Refinement via 3D Gaussian Splatting [25.780452115246245]
  We propose a novel test-time camera pose refinement framework, GSLoc. This framework enhances the localization accuracy of state-of-the-art absolute pose regression and scene coordinate regression methods. GSLoc obviates the need for training feature extractors or descriptors by operating directly on RGB images.
  arXiv  Detail & Related papers  (2024-08-20T17:58:23Z)
• Director3D: Real-world Camera Trajectory and 3D Scene Generation from Text [61.9973218744157]
  We introduce Director3D, a robust open-world text-to-3D generation framework, designed to generate both real-world 3D scenes and adaptive camera trajectories. Experiments demonstrate that Director3D outperforms existing methods, offering superior performance in real-world 3D generation.
  arXiv  Detail & Related papers  (2024-06-25T14:42:51Z)
• 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)
• 2D Gaussian Splatting for Geometrically Accurate Radiance Fields [50.056790168812114]
  3D Gaussian Splatting (3DGS) has recently revolutionized radiance field reconstruction, achieving high quality novel view synthesis and fast rendering speed without baking. We present 2D Gaussian Splatting (2DGS), a novel approach to model and reconstruct geometrically accurate radiance fields from multi-view images. We demonstrate that our differentiable terms allows for noise-free and detailed geometry reconstruction while maintaining competitive appearance quality, fast training speed, and real-time rendering.
  arXiv  Detail & Related papers  (2024-03-26T17:21:24Z)
• 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)
• SplaTAM: Splat, Track & Map 3D Gaussians for Dense RGB-D SLAM [48.190398577764284]
  SplaTAM is an approach to enable high-fidelity reconstruction from a single unposed RGB-D camera. It employs a simple online tracking and mapping system tailored to the underlying Gaussian representation. Experiments show that SplaTAM achieves up to 2x superior performance in camera pose estimation, map construction, and novel-view synthesis over existing methods.
  arXiv  Detail & Related papers  (2023-12-04T18:53:24Z)
• Urban Radiance Fields [77.43604458481637]
  We perform 3D reconstruction and novel view synthesis from data captured by scanning platforms commonly deployed for world mapping in urban outdoor environments. Our approach extends Neural Radiance Fields, which has been demonstrated to synthesize realistic novel images for small scenes in controlled settings. Each of these three extensions provides significant performance improvements in experiments on Street View data.
  arXiv  Detail & Related papers  (2021-11-29T15:58:16Z)

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.