A Benchmark Dataset and Evaluation for Non-Lambertian and Uncalibrated Photometric Stereo
Recent progress on photometric stereo extends the technique to deal with general materials and unknown illumination conditions. However, due to the lack of suitable benchmark data with ground truth shapes (normals), quantitative comparison and evaluation is difficult to achieve. In this paper, we first survey and categorize existing methods using a photometric stereo taxonomy emphasizing on non-Lambertian and uncalibrated methods. We then introduce the 'DiLiGenT' photometric stereo image dataset with calibrated Directional Lightings, objects of General reflectance, and 'ground Truth' shapes (normals). Based on our dataset, we quantitatively evaluate state-of-the-art photometric stereo methods for general non-Lambertian materials and unknown lightings to analyze their strengths and limitations.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Deep Photometric Stereo for Non-Lambertian Surfaces
This paper addresses the problem of photometric stereo, in both calibrated and uncalibrated scenarios, for non-Lambertian surfaces based on deep learning. We first introduce a fully convolutional deep network for calibra…
Solving Uncalibrated Photometric Stereo Using Fewer Images by Jointly Optimizing Low-rank Matrix Completion and Integrability
We introduce a new, integrated approach to uncalibrated photometric stereo. We perform 3D reconstruction of Lambertian objects using multiple images produced by unknown, directional light sources. We show how to formulat…
3D ReconstructionLow-Rank Matrix CompletionMatrix CompletionA New Perspective on Uncalibrated Photometric Stereo
We investigate the problem of reconstructing normals, albedo and lights of Lambertian surfaces in uncalibrated photometric stereo under the perspective projection model. Our analysis is based on establishing the integrab…
Self-calibrating Deep Photometric Stereo Networks
This paper proposes an uncalibrated photometric stereo method for non-Lambertian scenes based on deep learning. Unlike previous approaches that heavily rely on assumptions of specific reflectances and light source distri…
Deep LearningUncalibrated Photometric Stereo by Stepwise Optimization Using Principal Components of Isotropic BRDFs
The uncalibrated photometric stereo problem for non-Lambertian surfaces is challenging because of the large number of unknowns and its ill-posed nature stemming from unknown reflectance functions. We propose a model tha…