Efficient multi-view training for 3D Gaussian Splatting

TL;DR

This paper introduces efficient multi-view training techniques for 3D Gaussian Splatting, reducing variance and overhead, and improving rendering quality compared to traditional single-view methods.

Contribution

It proposes novel modifications to rasterization and loss functions enabling effective multi-view training in 3D Gaussian Splatting.

Findings

Multi-view training improves 3DGS performance.

Proposed methods reduce training overhead.

Enhanced rendering quality in experiments.

Abstract

3D Gaussian Splatting (3DGS) has emerged as a preferred choice alongside Neural Radiance Fields (NeRF) in inverse rendering due to its superior rendering speed. Currently, the common approach in 3DGS is to utilize "single-view" mini-batch training, where only one image is processed per iteration, in contrast to NeRF's "multi-view" mini-batch training, which leverages multiple images. We observe that such single-view training can lead to suboptimal optimization due to increased variance in mini-batch stochastic gradients, highlighting the necessity for multi-view training. However, implementing multi-view training in 3DGS poses challenges. Simply rendering multiple images per iteration incurs considerable overhead and may result in suboptimal Gaussian densification due to its reliance on single-view assumptions. To address these issues, we modify the rasterization process to minimize the overhead associated with multi-view training and propose a 3D distance-aware D-SSIM loss and multi-view adaptive density control that better suits multi-view scenarios. Our experiments demonstrate that the proposed methods significantly enhance the performance of 3DGS and its variants, freeing 3DGS from the constraints of single-view training.