79 research outputs found
Polarized 3D: High-Quality Depth Sensing with Polarization Cues
Coarse depth maps can be enhanced by using the shape information from polarization cues. We propose a framework to combine surface normals from polarization (hereafter polarization normals) with an aligned depth map. Polarization normals have not been used for depth enhancement before. This is because polarization normals suffer from physics-based artifacts, such as azimuthal ambiguity, refractive distortion and fronto-parallel signal degradation. We propose a framework to overcome these key challenges, allowing the benefits of polarization to be used to enhance depth maps. Our results demonstrate improvement with respect to state-of-the-art 3D reconstruction techniques.Charles Stark Draper Laboratory (Doctoral Fellowship)Singapore. Ministry of Education (Academic Research Foundation MOE2013-T2-1-159)Singapore. National Research Foundation (Singapore University of Technology and Design
NeuralMPS: Non-Lambertian Multispectral Photometric Stereo via Spectral Reflectance Decomposition
Multispectral photometric stereo(MPS) aims at recovering the surface normal
of a scene from a single-shot multispectral image captured under multispectral
illuminations. Existing MPS methods adopt the Lambertian reflectance model to
make the problem tractable, but it greatly limits their application to
real-world surfaces. In this paper, we propose a deep neural network named
NeuralMPS to solve the MPS problem under general non-Lambertian spectral
reflectances. Specifically, we present a spectral reflectance
decomposition(SRD) model to disentangle the spectral reflectance into geometric
components and spectral components. With this decomposition, we show that the
MPS problem for surfaces with a uniform material is equivalent to the
conventional photometric stereo(CPS) with unknown light intensities. In this
way, NeuralMPS reduces the difficulty of the non-Lambertian MPS problem by
leveraging the well-studied non-Lambertian CPS methods. Experiments on both
synthetic and real-world scenes demonstrate the effectiveness of our method
- …