Photometric Stereo using Gaussian Splatting and inverse rendering

• URL: http://arxiv.org/abs/2507.06684v1
• Date: Wed, 09 Jul 2025 09:22:24 GMT
• Title: Photometric Stereo using Gaussian Splatting and inverse rendering
• Authors: Matéo Ducastel, David Tschumperlé, Yvain Quéau,
• Abstract summary: We revisit the problem of photometric stereo by leveraging recent advances in 3D inverse rendering.<n>This allows us to parameterize the 3D scene to be reconstructed and optimize it in a more interpretable manner.
• Score: 2.416907802598482
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent state-of-the-art algorithms in photometric stereo rely on neural networks and operate either through prior learning or inverse rendering optimization. Here, we revisit the problem of calibrated photometric stereo by leveraging recent advances in 3D inverse rendering using the Gaussian Splatting formalism. This allows us to parameterize the 3D scene to be reconstructed and optimize it in a more interpretable manner. Our approach incorporates a simplified model for light representation and demonstrates the potential of the Gaussian Splatting rendering engine for the photometric stereo problem.

Related papers

• PS-GS: Gaussian Splatting for Multi-View Photometric Stereo [8.560379885301705]
  inverse rendering with multi-view photometric stereo (MVPS) yields more accurate 3D reconstructions than the inverse rendering approaches that rely on fixed environment illumination.<n>We introduce the Gaussian Splatting for Multi-view Photometric Stereo (PS-GS), which efficiently and jointly estimates the geometry, materials, and lighting of the object that is illuminated by diverse directional lights (multi-light)<n> Experiments on both synthetic and real datasets demonstrate that our method outperforms prior works in terms of reconstruction accuracy and computational efficiency.
  arXiv  Detail & Related papers  (2025-07-24T09:22:02Z)
• Visual SLAM with 3D Gaussian Primitives and Depth Priors Enabling Novel View Synthesis [11.236094544193605]
  Conventional geometry-based SLAM systems lack dense 3D reconstruction capabilities. We propose a real-time RGB-D SLAM system that incorporates a novel view synthesis technique, 3D Gaussian Splatting.
  arXiv  Detail & Related papers  (2024-08-10T21:23:08Z)
• 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)
• pixelSplat: 3D Gaussian Splats from Image Pairs for Scalable Generalizable 3D Reconstruction [26.72289913260324]
  pixelSplat is a feed-forward model that learns to reconstruct 3D radiance fields parameterized by 3D Gaussian primitives from pairs of images. Our model features real-time and memory-efficient rendering for scalable training as well as fast 3D reconstruction at inference time.
  arXiv  Detail & Related papers  (2023-12-19T17:03:50Z)
• GIR: 3D Gaussian Inverse Rendering for Relightable Scene Factorization [62.13932669494098]
  This paper presents a 3D Gaussian Inverse Rendering (GIR) method, employing 3D Gaussian representations to factorize the scene into material properties, light, and geometry. We compute the normal of each 3D Gaussian using the shortest eigenvector, with a directional masking scheme forcing accurate normal estimation without external supervision. We adopt an efficient voxel-based indirect illumination tracing scheme that stores direction-aware outgoing radiance in each 3D Gaussian to disentangle secondary illumination for approximating multi-bounce light transport.
  arXiv  Detail & Related papers  (2023-12-08T16:05:15Z)
• GPS-Gaussian: Generalizable Pixel-wise 3D Gaussian Splatting for Real-time Human Novel View Synthesis [70.24111297192057]
  We present a new approach, termed GPS-Gaussian, for synthesizing novel views of a character in a real-time manner. The proposed method enables 2K-resolution rendering under a sparse-view camera setting.
  arXiv  Detail & Related papers  (2023-12-04T18:59:55Z)
• RNb-NeuS: Reflectance and Normal-based Multi-View 3D Reconstruction [3.1820300989695833]
  This paper introduces a versatile paradigm for integrating multi-view reflectance and normal maps acquired through photometric stereo. Our approach employs a pixel-wise joint re- parameterization of reflectance and normal, considering them as a vector of radiances rendered under simulated, varying illumination. It significantly improves the detailed 3D reconstruction of areas with high curvature or low visibility.
  arXiv  Detail & Related papers  (2023-12-02T19:49:27Z)
• Multi-View Mesh Reconstruction with Neural Deferred Shading [0.8514420632209809]
  State-of-the-art methods use both neural surface representations and neural shading. We represent surfaces as triangle meshes and build a differentiable rendering pipeline around triangle rendering and neural shading. We evaluate our runtime on a public 3D reconstruction dataset and show that it can match the reconstruction accuracy of traditional baselines while surpassing them in optimization.
  arXiv  Detail & Related papers  (2022-12-08T16:29:46Z)
• Self-calibrating Photometric Stereo by Neural Inverse Rendering [88.67603644930466]
  This paper tackles the task of uncalibrated photometric stereo for 3D object reconstruction. We propose a new method that jointly optimize object shape, light directions, and light intensities. Our method demonstrates state-of-the-art accuracy in light estimation and shape recovery on real-world datasets.
  arXiv  Detail & Related papers  (2022-07-16T02:46:15Z)
• Extracting Triangular 3D Models, Materials, and Lighting From Images [59.33666140713829]
  We present an efficient method for joint optimization of materials and lighting from multi-view image observations. We leverage meshes with spatially-varying materials and environment that can be deployed in any traditional graphics engine.
  arXiv  Detail & Related papers  (2021-11-24T13:58:20Z)
• Neural Radiance Fields Approach to Deep Multi-View Photometric Stereo [103.08512487830669]
  We present a modern solution to the multi-view photometric stereo problem (MVPS) We procure the surface orientation using a photometric stereo (PS) image formation model and blend it with a multi-view neural radiance field representation to recover the object's surface geometry. Our method performs neural rendering of multi-view images while utilizing surface normals estimated by a deep photometric stereo network.
  arXiv  Detail & Related papers  (2021-10-11T20:20:03Z)

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.