Carlos Oliveira — accepted AI-conference papers

2022

Uncertainty-Aware Deep Multi-View Photometric Stereo

Berk Kaya, Suryansh Kumar, Carlos Oliveira, Vittorio Ferrari, Luc Van Gool

CVPR 2022poster

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 excellent at recovering high-frequency surface details, whereas multi-view stereo (MVS) can help remove the low-frequency d…

Cited by 45 PDF ↗Scholar ↗

2021

Uncalibrated Neural Inverse Rendering for Photometric Stereo of General Surfaces

Berk Kaya, Suryansh Kumar, Carlos Oliveira, Vittorio Ferrari, Luc Van Gool

CVPR 2021poster

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 methods either require exact light directions or ground-truth surface normals of the object or both. However, in practice, it…

Cited by 64 PDF ↗Scholar ↗