SplatSDF: Boosting SDF-NeRF via Architecture-Level Fusion with Gaussian Splats

Abstract

Signed distance-radiance field (SDF-NeRF) is a promising environment representation that offers both photo-realistic rendering and geometric reasoning such as proximity queries for collision avoidance. However, the slow training speed and convergence of SDF-NeRF hinder their use in practical robotic systems. We propose SplatSDF, a novel SDF-NeRF architecture that accelerates convergence using 3D Gaussian splats (3DGS), which can be quickly pre-trained. Unlike prior approaches that introduce a consistency loss between separate 3DGS and SDF-NeRF models, SplatSDF directly fuses 3DGS at an architectural level by consuming it as an input to SDF-NeRF during training. This is achieved using a novel sparse 3DGS fusion strategy that injects neural embeddings of 3DGS into SDF-NeRF around the object surface, while also permitting inference without 3DGS for minimal operation. Experimental results show SplatSDF achieves 3X faster convergence to the same geometric accuracy than the best baseline, and outperforms state-of-the-art SDF-NeRF methods in terms of chamfer distance and peak signal to noise ratio, unlike consistency loss-based approaches that in fact provide limited gains. We also present computational techniques for accelerating gradient and Hessian steps by 3X. We expect these improvements will contribute to deploying SDF-NeRF on practical systems.