Radiance field gradient scaling for unbiased near-camera training

Abstract

Methods and systems disclosed herein relate generally to radiance field gradient scaling for unbiased near-camera training. In a method, a processing device accesses an input image of a three-dimensional environment comprising a plurality of pixels, each pixel comprising a pixel color. The processing device determines a camera location based on the input image and a ray from the camera location in a direction of a pixel. The processing device integrates sampled information from a volumetric representation along the ray from the camera location to obtain an integrated color. The processing device trains a machine learning model configured to predict a density and a color, comprising minimizing a loss function using a scaling factor that is determined based on a distance between the camera location and a point along the ray. The processing device outputs the trained ML model for use in rendering an output image.