TSGS: Improving Gaussian Splatting for Transparent Surface Reconstruction via Normal and De-lighting Priors

• URL: http://arxiv.org/abs/2504.12799v1
• Date: Thu, 17 Apr 2025 10:00:09 GMT
• Title: TSGS: Improving Gaussian Splatting for Transparent Surface Reconstruction via Normal and De-lighting Priors
• Authors: Mingwei Li, Pu Pang, Hehe Fan, Hua Huang, Yi Yang,
• Abstract summary: We introduce Transparent Surface Gaussian Splatting (TSGS), a new framework that separates geometry learning from appearance refinement.<n>In the geometry learning stage, TSGS focuses on geometry by using specular-suppressed inputs to accurately represent surfaces.<n>To enhance depth inference, TSGS employs a first-surface depth extraction method.
• Score: 39.60777069381983
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Reconstructing transparent surfaces is essential for tasks such as robotic manipulation in labs, yet it poses a significant challenge for 3D reconstruction techniques like 3D Gaussian Splatting (3DGS). These methods often encounter a transparency-depth dilemma, where the pursuit of photorealistic rendering through standard $\alpha$-blending undermines geometric precision, resulting in considerable depth estimation errors for transparent materials. To address this issue, we introduce Transparent Surface Gaussian Splatting (TSGS), a new framework that separates geometry learning from appearance refinement. In the geometry learning stage, TSGS focuses on geometry by using specular-suppressed inputs to accurately represent surfaces. In the second stage, TSGS improves visual fidelity through anisotropic specular modeling, crucially maintaining the established opacity to ensure geometric accuracy. To enhance depth inference, TSGS employs a first-surface depth extraction method. This technique uses a sliding window over $\alpha$-blending weights to pinpoint the most likely surface location and calculates a robust weighted average depth. To evaluate the transparent surface reconstruction task under realistic conditions, we collect a TransLab dataset that includes complex transparent laboratory glassware. Extensive experiments on TransLab show that TSGS achieves accurate geometric reconstruction and realistic rendering of transparent objects simultaneously within the efficient 3DGS framework. Specifically, TSGS significantly surpasses current leading methods, achieving a 37.3% reduction in chamfer distance and an 8.0% improvement in F1 score compared to the top baseline. The code and dataset will be released at https://longxiang-ai.github.io/TSGS/.

Related papers

• Thin-Shell-SfT: Fine-Grained Monocular Non-rigid 3D Surface Tracking with Neural Deformation Fields [66.1612475655465]
  3D reconstruction of deformable surfaces from RGB videos is a challenging problem.<n>Existing methods use deformation models with statistical, neural, or physical priors.<n>We propose ThinShell-SfT, a new method for non-rigid 3D tracking meshes.
  arXiv  Detail & Related papers  (2025-03-25T18:00:46Z)
• MGSR: 2D/3D Mutual-boosted Gaussian Splatting for High-fidelity Surface Reconstruction under Various Light Conditions [6.4367384921445545]
  Novel view synthesis (NVS) and surface reconstruction (SR) are essential tasks in 3D Gaussian Splatting (3D-GS) We propose MGSR, a 2D/3D Mutual-boosted Gaussian splatting for Surface Reconstruction that enhances both rendering quality and 3D reconstruction accuracy. We evaluate MGSR on a diverse set of synthetic and real-world datasets, at both object and scene levels, demonstrating strong performance in rendering and surface reconstruction.
  arXiv  Detail & Related papers  (2025-03-07T07:06:47Z)
• GlossGau: Efficient Inverse Rendering for Glossy Surface with Anisotropic Spherical Gaussian [4.5442067197725]
  GlossGau is an efficient inverse rendering framework that reconstructs scenes with glossy surfaces while maintaining training and rendering speeds comparable to vanilla 3D-GS.<n> Experiments demonstrate that GlossGau achieves competitive or superior reconstruction on datasets with glossy surfaces.
  arXiv  Detail & Related papers  (2025-02-19T22:20:57Z)
• TranSplat: Surface Embedding-guided 3D Gaussian Splatting for Transparent Object Manipulation [10.957451368533302]
  TranSplat is a surface embedding-guided 3D Gaussian Splatting method tailored for transparent objects.<n>By integrating these surface embeddings with input RGB images, TranSplat effectively captures the complexities of transparent surfaces.
  arXiv  Detail & Related papers  (2025-02-11T03:43:56Z)
• GLS: Geometry-aware 3D Language Gaussian Splatting [16.13929985676661]
  This paper presents a unified framework of surface reconstruction and open-vocabulary segmentation based on 3DGS.<n>For indoor surface reconstruction, we introduce surface normal prior as a geometric cue to guide the rendered normal, and use the normal error to optimize the rendered depth.<n>For open-vocabulary segmentation, we employ 2D CLIP features to guide instance features and utilize DEVA masks to enhance their view consistency.
  arXiv  Detail & Related papers  (2024-11-27T05:21:34Z)
• Beyond Gaussians: Fast and High-Fidelity 3D Splatting with Linear Kernels [51.08794269211701]
  We introduce 3D Linear Splatting (3DLS), which replaces Gaussian kernels with linear kernels to achieve sharper and more precise results. 3DLS demonstrates state-of-the-art fidelity and accuracy, along with a 30% FPS improvement over baseline 3DGS.
  arXiv  Detail & Related papers  (2024-11-19T11:59:54Z)
• GeoSplatting: Towards Geometry Guided Gaussian Splatting for Physically-based Inverse Rendering [69.67264955234494]
  GeoSplatting is a novel hybrid representation that augments 3DGS with explicit geometric guidance and differentiable PBR equations. Comprehensive evaluations across diverse datasets demonstrate the superiority of GeoSplatting.
  arXiv  Detail & Related papers  (2024-10-31T17:57:07Z)
• GigaGS: Scaling up Planar-Based 3D Gaussians for Large Scene Surface Reconstruction [71.08607897266045]
  3D Gaussian Splatting (3DGS) has shown promising performance in novel view synthesis. We make the first attempt to tackle the challenging task of large-scale scene surface reconstruction. We propose GigaGS, the first work for high-quality surface reconstruction for large-scale scenes using 3DGS.
  arXiv  Detail & Related papers  (2024-09-10T17:51:39Z)
• SAGS: Structure-Aware 3D Gaussian Splatting [53.6730827668389]
  We propose a structure-aware Gaussian Splatting method (SAGS) that implicitly encodes the geometry of the scene. SAGS reflects to state-of-the-art rendering performance and reduced storage requirements on benchmark novel-view synthesis datasets.
  arXiv  Detail & Related papers  (2024-04-29T23:26:30Z)

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.