Fast Converging 3D Gaussian Splatting for 1-Minute Reconstruction

TL;DR

This paper introduces a rapid 3D Gaussian Splatting reconstruction pipeline capable of producing high-quality 3D models within one minute, suitable for challenging heterogeneous pose scenarios, and achieves top performance in a competitive benchmark.

Contribution

The paper develops a two-stage, fast 3DGS reconstruction method that handles noisy and accurate poses, combining novel optimization, neural representations, and multi-view consistency techniques.

Findings

Achieved top PSNR of 28.43 in the challenge

Converged within one minute for complex scenes

Outperformed existing methods in speed and quality

Abstract

We present a fast 3DGS reconstruction pipeline designed to converge within one minute, developed for the SIGGRAPH Asia 3DGS Fast Reconstruction Challenge. The challenge consists of an initial round using SLAM-generated camera poses (with noisy trajectories) and a final round using COLMAP poses (highly accurate). To robustly handle these heterogeneous settings, we develop a two-stage solution. In the first round, we use reverse per-Gaussian parallel optimization and compact forward splatting based on Taming-GS and Speedy-splat, load-balanced tiling, an anchor-based Neural-Gaussian representation enabling rapid convergence with fewer learnable parameters, initialization from monocular depth and partially from feed-forward 3DGS models, and a global pose refinement module for noisy SLAM trajectories. In the final round, the accurate COLMAP poses change the optimization landscape; we disable pose refinement, revert from Neural-Gaussians back to standard 3DGS to eliminate MLP inference overhead, introduce multi-view consistency-guided Gaussian splitting inspired by Fast-GS, and introduce a depth estimator to supervise the rendered depth. Together, these techniques enable high-fidelity reconstruction under a strict one-minute budget. Our method achieved the top performance with a PSNR of 28.43 and ranked first in the competition.