PlenopticPoints: Rasterizing Neural Feature Points for High-Quality Novel View Synthesis

Abstract

This paper presents a point-based, neural rendering approach for complex real-world objects from a set of photographs. Our method is specifically geared towards representing fine detail and reflective surface characteristics at improved quality over current state-of-the-art methods. From the photographs, we create a 3D point model based on optimized neural feature points located on a regular grid. For rendering, we employ view-dependent spherical harmonics shading, differentiable rasterization, and a deep neural rendering network. By combining a point-based approach and novel regularizers, our method is able to accurately represent local detail such as fine geometry and high-frequency texture while at the same time convincingly interpolating unseen viewpoints during inference. Our method achieves about 7 frames per second at 800×800 pixel output resolution on commodity hardware, putting it within reach for real-time rendering applications.