Geo-NI: Geometry-aware Neural Interpolation for Light Field Rendering

Gaochang Wu, Yuemei Zhou, Lu Fang, Yebin Liu, Tianyou Chai

Abstract: We present a novel Geometry-aware Neural Interpolation (Geo-NI) framework for light field rendering. Previous learning-based approaches either perform direct interpolation via neural networks, which we dubbed Neural Interpolation (NI), or explore scene geometry for novel view synthesis, also known as Depth Image-Based Rendering (DIBR). Both kinds of approaches have their own strengths and weaknesses in addressing non-Lambert effect and large disparity problems. In this paper, we incorporate the ideas behind these two kinds of approaches by launching the NI within a specific DIBR pipeline. Specifically, a DIBR network in the proposed Geo-NI serves to construct a novel reconstruction cost volume for neural interpolated light fields sheared by different depth hypotheses. The reconstruction cost can be interpreted as an indicator reflecting the reconstruction quality under a certain depth hypothesis, and is further applied to guide the rendering of the final high angular resolution light field. To implement the Geo-NI framework more practically, we further propose an efficient modeling strategy to encode high-dimensional cost volumes using a lower-dimension network. By combining the superiorities of NI and DIBR, the proposed Geo-NI is able to render views with large disparities with the help of scene geometry while also reconstructing the non-Lambertian effect when depth is prone to be ambiguous. Extensive experiments on various datasets demonstrate the superior performance of the proposed geometry-aware light field rendering framework.

External IDs:doi:10.1109/tpami.2025.3594705