A Neural Reflectance Field Model for Accurate Relighting in RTI Applications
Year: 2025
Type: article
Abstract: Reflectance Transformation Imaging (RTI) is a computational photography technique in which an object is acquired from a fixed point-of-view with different light directions. The aim is to estimate the light transport function at each point so that the object can be interactively relighted in a physically-accurate way, revealing its surface characteristics. In this paper, we propose a novel RTI approach describing surface reflectance as an implicit neural representation acting as a ”relightable image” for a specific object. We propose to represent the light transport function with a Neural Reflectance Field (NRF) model, feeding it with pixel coordinates, light direction, and a latent vector encoding the per-pixel reflectance in a neighbourhood. These vectors, computed during training, allow a more accurate relighting than a pure implicit representation (i.e., relying only on positional encoding) enabling the NRF to handle complex surface shadings. Moreover, they can be efficiently stored with the learned NRF for compression and transmission. As an additional contribution, we propose a novel synthetic dataset containing objects of various shapes and materials created with a physically based rendering software. An extensive experimental section shows that the proposed NRF accurately models the light transport function for challenging datasets in synthetic and real-world scenarios.
Source: ACM Transactions on Graphics
DOI: 10.1145/3759452
Institution: Ca' Foscari University of Venice
Language: English
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4413792338