Fast Implicit Neural Light Field Representation via Geometric Decomposition and Multi-Resolution Low-Rank Features

We present Light Field Primitives (LFP), a formulation for novel view synthesis that replaces the dense ray database with a compact set of differentiable primitives in the classical two-plane parameterization. Each primitive condenses a group of rays into one learned record, and its response to a query is governed by how closely that query belongs to the group. Rendering a camera ray then reduces to compositing all responses it elicits, and a scene can be optimized directly from posed images and rendered in real time with rays. Beyond its competitive performance on standard benchmarks, the main advantage of LFP is structural: its primitives reside directly in the 4D ray space, so optical and appearance effects that are already operations on the light field become behaviors of a single shared renderer. With minimal changes to that renderer, LFP supports multi-scale anti-aliasing, defocus deblurring with refocusing, rendering for fisheye cameras, and even transparent object reconstruction with ray refraction, matching specialized frameworks that devote substantial machinery to these effects.

Recent advances in neural scene representations enable photorealistic novel-view synthesis, yet most methods remain tightly coupled to a single rendering paradigm, limiting their versatility and integration with conventional graphics workflows. We introduce Floating Radiance Networks (FlaRe), a neural scene representation combining explicit ray-traceable geometry with continuous neural radiance functions. A scene is represented by floating planar generalized Gaussian primitives, each carrying a compact latent descriptor of a local radiance field. A lightweight decoder shared across the scene maps this descriptor, local surface coordinates, and viewing direction to color and opacity. This formulation preserves the expressiveness of neural fields while providing an explicitly addressable structure that can be efficiently queried and manipulated. Hardware-accelerated primitive intersections enable interactive rendering and recursive ray-tracing, including reflections, refractions, transparency, and shadows. The same representation further supports primitive-level deformation, mesh extraction, and appearance stylization directly in its learned descriptor space. Experiments across standard reconstruction benchmarks demonstrate competitive rendering quality while using a compact set of primitives. Together, these results establish FlaRe as a versatile representation that brings high-fidelity neural rendering, ray-tracing, geometric manipulation, and appearance editing into a unified scene model. Source code is available online. Source code can be found at: https://github.com/KByrski/FlaRe

Light-field disparity estimation requires global consistency in smooth or textureless regions and local precision near occlusion boundaries, thin structures, and abrupt depth transitions. Existing methods address these requirements through EPI matching, cost-volume or focal-stack construction, view aggregation, or direct convolutional regression, often relying on local windows, discrete disparity hypotheses, memory-intensive volumes, or attention-based aggregation. We instead formulate disparity estimation at the field level, predicting disparity from globally and locally updated EPI-derived latent features without explicitly constructing a disparity volume. We introduce FreqLF, an EPI-guided Fourier-local framework that encodes angular parallax cues from horizontal and vertical EPI stacks together with central-view appearance features. These cues are projected into a latent field and updated through stacked hybrid Fourier-local layers. Fourier low-mode updates enable global feature interaction, while local convolutions preserve spatial variations needed for fine disparity detail. A coordinate-conditioned Gaussian-mixture decoder then predicts disparity, using the mixture mean as the final estimate. Experiments on the HCI 4D Light Field Benchmark show that FreqLF approaches the accuracy of strong supervised baselines while avoiding explicit cost-volume construction in the base model. Ablations confirm the complementary roles of the Fourier and local branches, and scaling experiments demonstrate practical behavior across spatial resolutions. These results suggest that Fourier-local latent field learning is a competitive alternative for light-field disparity estimation. The code will be published soon.