RotatedMVPS: Multi-view Photometric Stereo with Rotated Natural Light

• URL: http://arxiv.org/abs/2508.04366v1
• Date: Wed, 06 Aug 2025 12:02:57 GMT
• Title: RotatedMVPS: Multi-view Photometric Stereo with Rotated Natural Light
• Authors: Songyun Yang, Yufei Han, Jilong Zhang, Kongming Liang, Peng Yu, Zhaowei Qu, Heng Guo,
• Abstract summary: Multiview photometric stereo (MVPS) seeks to recover high-fidelity surface shapes and reflectances from images captured under varying views and illuminations.<n>We propose RotatedMVPS to solve shape and reflectance recovery under rotated natural light, achievable with a practical rotation stage.
• Score: 13.01996067580538
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multiview photometric stereo (MVPS) seeks to recover high-fidelity surface shapes and reflectances from images captured under varying views and illuminations. However, existing MVPS methods often require controlled darkroom settings for varying illuminations or overlook the recovery of reflectances and illuminations properties, limiting their applicability in natural illumination scenarios and downstream inverse rendering tasks. In this paper, we propose RotatedMVPS to solve shape and reflectance recovery under rotated natural light, achievable with a practical rotation stage. By ensuring light consistency across different camera and object poses, our method reduces the unknowns associated with complex environment light. Furthermore, we integrate data priors from off-the-shelf learning-based single-view photometric stereo methods into our MVPS framework, significantly enhancing the accuracy of shape and reflectance recovery. Experimental results on both synthetic and real-world datasets demonstrate the effectiveness of our approach.

Related papers

• Dereflection Any Image with Diffusion Priors and Diversified Data [86.15504914121226]
  We propose a comprehensive solution with an efficient data preparation pipeline and a generalizable model for robust reflection removal.<n>First, we introduce a dataset named Diverse Reflection Removal (DRR) created by randomly rotating reflective mediums in target scenes.<n>Second, we propose a diffusion-based framework with one-step diffusion for deterministic outputs and fast inference.
  arXiv  Detail & Related papers  (2025-03-21T17:48:14Z)
• DifFRelight: Diffusion-Based Facial Performance Relighting [12.909429637057343]
  We present a novel framework for free-viewpoint facial performance relighting using diffusion-based image-to-image translation. We train a diffusion model for precise lighting control, enabling high-fidelity relit facial images from flat-lit inputs. The model accurately reproduces complex lighting effects like eye reflections, subsurface scattering, self-shadowing, and translucency.
  arXiv  Detail & Related papers  (2024-10-10T17:56:44Z)
• MERLiN: Single-Shot Material Estimation and Relighting for Photometric Stereo [26.032964551717548]
  Photometric stereo typically demands intricate data acquisition setups involving multiple light sources to recover surface normals accurately. We propose MERLiN, an attention-based hourglass network that integrates single image-based inverse rendering and relighting within a single unified framework.
  arXiv  Detail & Related papers  (2024-09-01T09:32:03Z)
• PIR: Photometric Inverse Rendering with Shading Cues Modeling and Surface Reflectance Regularization [46.146783750386994]
  We propose a new method for neural inverse rendering.<n>Our method jointly optimize the light source position to account for the self-shadows in images.<n>To enhance surface reflectance decomposition, we introduce a new regularization.
  arXiv  Detail & Related papers  (2024-08-13T11:39:14Z)
• Factored-NeuS: Reconstructing Surfaces, Illumination, and Materials of Possibly Glossy Objects [58.25772313290338]
  We develop a method that recovers the surface, materials, and illumination of a scene from its posed multi-view images.<n>It does not require any additional data and can handle glossy objects or bright lighting.
  arXiv  Detail & Related papers  (2023-05-29T07:44:19Z)
• WildLight: In-the-wild Inverse Rendering with a Flashlight [77.31815397135381]
  We propose a practical photometric solution for in-the-wild inverse rendering under unknown ambient lighting. Our system recovers scene geometry and reflectance using only multi-view images captured by a smartphone. We demonstrate by extensive experiments that our method is easy to implement, casual to set up, and consistently outperforms existing in-the-wild inverse rendering techniques.
  arXiv  Detail & Related papers  (2023-03-24T17:59:56Z)
• 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)
• Leveraging Spatial and Photometric Context for Calibrated Non-Lambertian Photometric Stereo [61.6260594326246]
  We introduce an efficient fully-convolutional architecture that can leverage both spatial and photometric context simultaneously. Using separable 4D convolutions and 2D heat-maps reduces the size and makes more efficient.
  arXiv  Detail & Related papers  (2021-03-22T18:06:58Z)
• Multi-View Photometric Stereo: A Robust Solution and Benchmark Dataset for Spatially Varying Isotropic Materials [65.95928593628128]
  We present a method to capture both 3D shape and spatially varying reflectance with a multi-view photometric stereo technique. Our algorithm is suitable for perspective cameras and nearby point light sources.
  arXiv  Detail & Related papers  (2020-01-18T12:26:22Z)

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.