Physics-Free Spectrally Multiplexed Photometric Stereo under Unknown Spectral Composition
This addresses the challenge of dynamic surface recovery in uncalibrated setups for applications in computer vision and related domains, representing a notable advancement over traditional methods.
The paper tackles the problem of recovering surface normals of dynamic surfaces without calibrated lighting or sensors by introducing a physics-free spectrally multiplexed photometric stereo approach, achieving results comparable to calibrated methods on a new benchmark dataset.
In this paper, we present a groundbreaking spectrally multiplexed photometric stereo approach for recovering surface normals of dynamic surfaces without the need for calibrated lighting or sensors, a notable advancement in the field traditionally hindered by stringent prerequisites and spectral ambiguity. By embracing spectral ambiguity as an advantage, our technique enables the generation of training data without specialized multispectral rendering frameworks. We introduce a unique, physics-free network architecture, SpectraM-PS, that effectively processes multiplexed images to determine surface normals across a wide range of conditions and material types, without relying on specific physically-based knowledge. Additionally, we establish the first benchmark dataset, SpectraM14, for spectrally multiplexed photometric stereo, facilitating comprehensive evaluations against existing calibrated methods. Our contributions significantly enhance the capabilities for dynamic surface recovery, particularly in uncalibrated setups, marking a pivotal step forward in the application of photometric stereo across various domains.