7 papers
Multi-View Photometric Stereo Revisited
Berk Kaya, Suryansh Kumar, Carlos Oliveira +2
Multi-view photometric stereo (MVPS) is a preferred method for detailed and precise 3D acquisition of an object from images. Although popular methods for MVPS can provide outstandi…
Uncertainty-Aware Deep Multi-View Photometric Stereo
Berk Kaya, Suryansh Kumar, Carlos Oliveira +2
This paper presents a simple and effective solution to the longstanding classical multi-view photometric stereo (MVPS) problem. It is well-known that photometric stereo (PS) is exc…
Neural Architecture Search for Efficient Uncalibrated Deep Photometric Stereo
Francesco Sarno, Suryansh Kumar, Berk Kaya +3
We present an automated machine learning approach for uncalibrated photometric stereo (PS). Our work aims at discovering lightweight and computationally efficient PS neural network…
Neural Radiance Fields Approach to Deep Multi-View Photometric Stereo
Berk Kaya, Suryansh Kumar, Francesco Sarno +2
We present a modern solution to the multi-view photometric stereo problem (MVPS). Our work suitably exploits the image formation model in a MVPS experimental setup to recover the d…
Uncalibrated Neural Inverse Rendering for Photometric Stereo of General Surfaces
Berk Kaya, Suryansh Kumar, Carlos Oliveira +2
This paper presents an uncalibrated deep neural network framework for the photometric stereo problem. For training models to solve the problem, existing neural network-based method…
Self-Supervised 2D Image to 3D Shape Translation with Disentangled Representations
Berk Kaya, Radu Timofte
We present a framework to translate between 2D image views and 3D object shapes. Recent progress in deep learning enabled us to learn structure-aware representations from a scene.…