Search Results for author:
Found 8 papers, 1 papers with code
Multi-View Photometric Stereo Revisited
The proposed approach in this paper exploits the benefit of uncertainty modeling in a deep neural network for a reliable fusion of photometric stereo (PS) and multi-view stereo (MVS) network predictions.
Uncertainty-Aware Deep Multi-View Photometric Stereo
At each pixel, our approach either selects or discards deep-PS and deep-MVS network prediction depending on the prediction uncertainty measure.
Neural Architecture Search for Efficient Uncalibrated Deep Photometric Stereo
We then perform a continuous relaxation of this search space and present a gradient-based optimization strategy to find an efficient light calibration and normal estimation network.
Neural Radiance Fields Approach to Deep Multi-View Photometric Stereo
Our method performs neural rendering of multi-view images while utilizing surface normals estimated by a deep photometric stereo network.
Uncalibrated Neural Inverse Rendering for Photometric Stereo of General Surfaces
This paper presents an uncalibrated deep neural network framework for the photometric stereo problem.
Self-Supervised 2D Image to 3D Shape Translation with Disentangled Representations
In this paper, we propose SIST, a Self-supervised Image to Shape Translation framework that fulfills three tasks: (i) reconstructing the 3D shape from a single image; (ii) learning disentangled representations for shape, appearance and viewpoint; and (iii) generating a realistic RGB image from these independent factors.
Towards Spectral Estimation from a Single RGB Image in the Wild
In contrast to the current literature, we address the problem of estimating the spectrum from a single common trichromatic RGB image obtained under unconstrained settings (e. g. unknown camera parameters, unknown scene radiance, unknown scene contents).
Spectral Estimation From A Single Rgb Image
Spectral Reconstruction
EndNet: Sparse AutoEncoder Network for Endmember Extraction and Hyperspectral Unmixing
Data acquired from multi-channel sensors is a highly valuable asset to interpret the environment for a variety of remote sensing applications.
