Ascertaining the ideality of photometric stereo datasets under unknown lighting

TL;DR

This paper evaluates the assumptions of photometric stereo datasets under unknown lighting conditions, proposing algorithms to assess dataset reliability and improve 3D reconstruction accuracy in practical scenarios.

Contribution

It introduces algorithms to determine the ideality of photometric stereo datasets and select optimal images for better reconstruction under non-ideal conditions.

Findings

Algorithms effectively identify reliable images for reconstruction.

Assessment methods improve 3D model accuracy in challenging scenarios.

Proposed techniques handle non-Lambertian surfaces and close lighting conditions.

Abstract

The standard photometric stereo model makes several assumptions that are rarely verified in experimental datasets. In particular, the observed object should behave as a Lambertian reflector and the light sources should be positioned at an infinite distance from it, along a known direction. Even when Lambert's law is approximately fulfilled, an accurate assessment of the relative position between the light source and the target is often unavailable in real situations. The Hayakawa procedure is a computational method for estimating such information directly from the data images. It occasionally breaks down when some of the available images excessively deviate from ideality. This is generally due to observing a non Lambertian surface, or illuminating it from a close distance, or both. Indeed, in narrow shooting scenarios, typical, e.g., of archaeological excavation sites, it is impossible to position a flashlight at a sufficient distance from the observed surface. It is then necessary to understand if a given dataset is reliable and which images should be selected to better reconstruct the target. In this paper, we propose some algorithms to perform this task and explore their effectiveness.