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 photorealistic 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 3 times 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 3 times. We expect these improvements will contribute to deploying SDF-NeRF on practical systems.