SGAD-SLAM: Splatting Gaussians at Adjusted Depth for Better Radiance Fields in RGBD SLAM

• URL: http://arxiv.org/abs/2603.21055v1
• Date: Sun, 22 Mar 2026 04:42:02 GMT
• Title: SGAD-SLAM: Splatting Gaussians at Adjusted Depth for Better Radiance Fields in RGBD SLAM
• Abstract summary: Current methods usually use 3D Gaussians or view-tied 3D Gaussians to represent radiance fields in tracking and mapping.<n>We adopt pixel-aligned Gaussians but allow each Gaussian to adjust its position along its ray to maximize the rendering quality.<n>We report our evaluations on widely used benchmarks, justify our designs, and show advantages over the latest methods in view rendering, camera tracking, runtime, and storage complexity.
• Score: 46.618504092695254
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D Gaussian Splatting (3DGS) has made remarkable progress in RGBD SLAM. Current methods usually use 3D Gaussians or view-tied 3D Gaussians to represent radiance fields in tracking and mapping. However, these Gaussians are either too flexible or too limited in movements, resulting in slow convergence or limited rendering quality. To resolve this issue, we adopt pixel-aligned Gaussians but allow each Gaussian to adjust its position along its ray to maximize the rendering quality, even if Gaussians are simplified to improve system scalability. To speed up the tracking, we model the depth distribution around each pixel as a Gaussian distribution, and then use these distributions to align each frame to the 3D scene quickly. We report our evaluations on widely used benchmarks, justify our designs, and show advantages over the latest methods in view rendering, camera tracking, runtime, and storage complexity. Please see our project page for code and videos at https://machineperceptionlab.github.io/SGAD-SLAM-Project .

Related papers

• Speeding Up the Learning of 3D Gaussians with Much Shorter Gaussian Lists [49.743765571852485]
  We propose novel training strategies and losses to shorten each Gaussian list used to render a pixel.<n>Specifically, we shrink the size of each Gaussian by resetting their scales regularly.<n>We also introduce an entropy constraint on the alpha blending procedure to sharpen the weight distribution of Gaussians along each ray.
  arXiv  Detail & Related papers  (2026-03-10T07:03:44Z)
• VTGaussian-SLAM: RGBD SLAM for Large Scale Scenes with Splatting View-Tied 3D Gaussians [27.62796825514193]
  State-of-the-art methods employ 3D Gaussians to represent a scene, and render these Gaussians through splatting for higher efficiency and better rendering.<n>These methods cannot scale up to extremely large scenes, due to the inefficient tracking and mapping strategies.<n>To resolve this issue, we propose novel tracking and mapping strategies to work with a novel 3D representation, dubbed view-tied 3D Gaussians.
  arXiv  Detail & Related papers  (2025-06-03T10:59:19Z)
• 3DGEER: Exact and Efficient Volumetric Rendering with 3D Gaussians [15.776720879897345]
  We introduce 3DGEER, an Exact and Efficient Volumetric Gaussian Rendering method.<n>Our method consistently outperforms prior methods, establishing a new state-of-the-art in real-time neural rendering.
  arXiv  Detail & Related papers  (2025-05-29T22:52:51Z)
• Pushing Rendering Boundaries: Hard Gaussian Splatting [72.28941128988292]
  3D Gaussian Splatting (3DGS) has demonstrated impressive Novel View Synthesis (NVS) results in a real-time rendering manner.<n>We propose Hard Gaussian Splatting, dubbed HGS, which considers multi-view significant positional gradients and rendering errors to grow hard Gaussians.<n>Our method achieves state-of-the-art rendering quality while maintaining real-time efficiency.
  arXiv  Detail & Related papers  (2024-12-06T07:42:47Z)
• PixelGaussian: Generalizable 3D Gaussian Reconstruction from Arbitrary Views [116.10577967146762]
  PixelGaussian is an efficient framework for learning generalizable 3D Gaussian reconstruction from arbitrary views. Our method achieves state-of-the-art performance with good generalization to various numbers of views.
  arXiv  Detail & Related papers  (2024-10-24T17:59:58Z)
• Dynamic Gaussian Marbles for Novel View Synthesis of Casual Monocular Videos [58.22272760132996]
  We show that existing 4D Gaussian methods dramatically fail in this setup because the monocular setting is underconstrained. We propose Dynamic Gaussian Marbles, which consist of three core modifications that target the difficulties of the monocular setting. We evaluate on the Nvidia Dynamic Scenes dataset and the DyCheck iPhone dataset, and show that Gaussian Marbles significantly outperforms other Gaussian baselines in quality.
  arXiv  Detail & Related papers  (2024-06-26T19:37:07Z)
• GSGAN: Adversarial Learning for Hierarchical Generation of 3D Gaussian Splats [20.833116566243408]
  In this paper, we exploit Gaussian as a 3D representation for 3D GANs by leveraging its efficient and explicit characteristics. We introduce a generator architecture with a hierarchical multi-scale Gaussian representation that effectively regularizes the position and scale of generated Gaussians. Experimental results demonstrate that ours achieves a significantly faster rendering speed (x100) compared to state-of-the-art 3D consistent GANs.
  arXiv  Detail & Related papers  (2024-06-05T05:52:20Z)
• RTG-SLAM: Real-time 3D Reconstruction at Scale using Gaussian Splatting [51.51310922527121]
  We present a real-time 3D reconstruction system with an RGBD camera for large-scale environments using Gaussian splatting. We force each Gaussian to be either opaque or nearly transparent, with the opaque ones fitting the surface and dominant colors, and transparent ones fitting residual colors. We show real-time reconstructions of a variety of large scenes and show superior performance in the realism of novel view synthesis and camera tracking accuracy.
  arXiv  Detail & Related papers  (2024-04-30T16:54:59Z)
• LeanGaussian: Breaking Pixel or Point Cloud Correspondence in Modeling 3D Gaussians [11.71048049090424]
  We introduce LeanGaussian, a novel approach that treats each query in deformable Transformer as one 3D Gaussian ellipsoid.<n>We leverage deformable decoder to iteratively refine the Gaussians layer-by-layer with the image features as keys and values.<n>Our approach outperforms prior methods by approximately 6.1%, achieving a PSNR of 25.44 and 22.36, respectively.
  arXiv  Detail & Related papers  (2024-04-25T04:18:59Z)

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.