Work

Neural light field representation and reconstruction based on a ray displacement field

Year: 2026

Type: article

Abstract: High-quality light field (LF) acquisition involves a trade-off between spatial and angular resolution. Hybrid camera systems offer a practical solution but present challenges for dense reconstruction due to sparse angular sampling and complex occlusions. To address these issues, this paper proposes a geometry-aware implicit neural representation (INR) framework for LF reconstruction. Distinct from traditional discrete representations or generic ray-space embeddings, we introduce a compact, continuous representation that combines a radiance field with a geometry module, where scalar disparity provides an ideal geometric interpretation and a ray-displacement field serves as the practical realization for occlusion-aware reconstruction. This framework leverages the global epipolar geometry of the 2D LF grid while utilizing the displacement field to correct local geometric inconsistencies caused by occlusions and non-Lambertian effects. By coupling these fields with a radiance network, our method enables end-to-end differentiable optimization from sparse, multi-resolution inputs without relying on large-scale external training datasets. Experiments on hybrid camera data fusion and spatial-angular super-resolution tasks demonstrate that our approach preserves high-frequency details and geometric consistency.

Source: Optics Express

DOI: 10.1364/oe.583881

Authors: Chang Liu, Ligen Shi, Lina Wu, Kaiwen Yu, Jun Qiu

Institutions: Beijing Information Science & Technology University, Inner Mongolia University

Language: English

Topic: Advanced Vision and Imaging

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,270

Funder: National Natural Science Foundation of China