ShadowGS: Shadow-Aware 3D Gaussian Splatting for Satellite Imagery

• URL: http://arxiv.org/abs/2601.00939v1
• Date: Sun, 04 Jan 2026 06:33:59 GMT
• Title: ShadowGS: Shadow-Aware 3D Gaussian Splatting for Satellite Imagery
• Authors: Feng Luo, Hongbo Pan, Xiang Yang, Baoyu Jiang, Fengqing Liu, Tao Huang,
• Abstract summary: We propose ShadowGS, a novel framework based on 3DGS.<n>It precisely model geometrically consistent shadows while maintaining efficient rendering.<n>It exhibits robust performance across various settings, including RGB, pansharpened, and sparse-view satellite inputs.
• Score: 7.33738775121714
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 3D Gaussian Splatting (3DGS) has emerged as a novel paradigm for 3D reconstruction from satellite imagery. However, in multi-temporal satellite images, prevalent shadows exhibit significant inconsistencies due to varying illumination conditions. To address this, we propose ShadowGS, a novel framework based on 3DGS. It leverages a physics-based rendering equation from remote sensing, combined with an efficient ray marching technique, to precisely model geometrically consistent shadows while maintaining efficient rendering. Additionally, it effectively disentangles different illumination components and apparent attributes in the scene. Furthermore, we introduce a shadow consistency constraint that significantly enhances the geometric accuracy of 3D reconstruction. We also incorporate a novel shadow map prior to improve performance with sparse-view inputs. Extensive experiments demonstrate that ShadowGS outperforms current state-of-the-art methods in shadow decoupling accuracy, 3D reconstruction precision, and novel view synthesis quality, with only a few minutes of training. ShadowGS exhibits robust performance across various settings, including RGB, pansharpened, and sparse-view satellite inputs.

Related papers

• Animated 3DGS Avatars in Diverse Scenes with Consistent Lighting and Shadows [23.490603624391095]
  We present a method for consistent lighting and shadows when animated 3D Gaussian Splatting (3DGS) avatars interact with 3DGS scenes or with dynamic objects inserted into otherwise static scenes.<n>Key contribution is Deep Gaussian Shadow Maps (DGSM), a modern analogue of the classical shadow mapping algorithm tailored to the volumetric 3DGS representation.<n>We demonstrate environment consistent lighting for avatars from AvatarX and ActorsHQ, composited into ScanNet++, DL3DV, and SuperSplat scenes, and show interactions with inserted objects.
  arXiv  Detail & Related papers  (2026-01-04T20:42:06Z)
• 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)
• ComGS: Efficient 3D Object-Scene Composition via Surface Octahedral Probes [46.83857963152283]
  Gaussian Splatting (GS) enables immersive rendering, but realistic 3D object-scene composition remains challenging.<n>We propose ComGS, a novel 3D object-scene composition framework.<n>Our method achieves high-quality, real-time rendering at around 28 FPS, produces visually harmonious results with vivid shadows, and requires only 36 seconds for editing.
  arXiv  Detail & Related papers  (2025-10-09T03:10:41Z)
• 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)
• 3D Convex Splatting: Radiance Field Rendering with 3D Smooth Convexes [87.01284850604495]
  We introduce 3D Convexting (3DCS), which leverages 3D smooth convexes as primitives for modeling geometrically-meaningful radiance fields from multiview images.<n>3DCS achieves superior performance over 3DGS on benchmarks such as MipNeizer, Tanks and Temples, and Deep Blending.<n>Our results highlight the potential of 3D Convexting to become the new standard for high-quality scene reconstruction.
  arXiv  Detail & Related papers  (2024-11-22T14:31:39Z)
• GUS-IR: Gaussian Splatting with Unified Shading for Inverse Rendering [83.69136534797686]
  We present GUS-IR, a novel framework designed to address the inverse rendering problem for complicated scenes featuring rough and glossy surfaces. This paper starts by analyzing and comparing two prominent shading techniques popularly used for inverse rendering, forward shading and deferred shading. We propose a unified shading solution that combines the advantages of both techniques for better decomposition.
  arXiv  Detail & Related papers  (2024-11-12T01:51:05Z)
• RNG: Relightable Neural Gaussians [19.197099019727826]
  We propose a novel 3DGS-based framework that enables the relighting of objects with both hard surfaces or soft boundaries.<n>We also introduce a shadow cue, as well as a depth refinement network to improve shadow accuracy.<n>Our method achieves significantly faster training (1.3 hours) and rendering (60 frames per second) compared to a prior method.
  arXiv  Detail & Related papers  (2024-09-29T13:32:24Z)
• 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.<n>We present 2D Gaussian Splatting (2DGS), a novel approach to model and reconstruct geometrically accurate radiance fields from multi-view images.<n>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)
• GS-IR: 3D Gaussian Splatting for Inverse Rendering [71.14234327414086]
  We propose GS-IR, a novel inverse rendering approach based on 3D Gaussian Splatting (GS) We extend GS, a top-performance representation for novel view synthesis, to estimate scene geometry, surface material, and environment illumination from multi-view images captured under unknown lighting conditions. The flexible and expressive GS representation allows us to achieve fast and compact geometry reconstruction, photorealistic novel view synthesis, and effective physically-based rendering.
  arXiv  Detail & Related papers  (2023-11-26T02:35:09Z)
• Controllable Shadow Generation Using Pixel Height Maps [58.59256060452418]
  Physics-based shadow rendering methods require 3D geometries, which are not always available. Deep learning-based shadow synthesis methods learn a mapping from the light information to an object's shadow without explicitly modeling the shadow geometry. We introduce pixel heigh, a novel geometry representation that encodes the correlations between objects, ground, and camera pose.
  arXiv  Detail & Related papers  (2022-07-12T08:29:51Z)
• Towards Learning Neural Representations from Shadows [11.60149896896201]
  We present a method that learns neural scene representations from only shadows present in the scene. Our framework is highly generalizable and can work alongside existing 3D reconstruction techniques.
  arXiv  Detail & Related papers  (2022-03-29T23:13:41Z)

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.