Unposed 3DGS Reconstruction with Probabilistic Procrustes Mapping

TL;DR

This paper introduces a novel framework for unposed 3D Gaussian Splatting reconstruction that integrates pretrained MVS priors with probabilistic Procrustes mapping, enabling accurate scene and pose estimation from large outdoor image datasets.

Contribution

It proposes a probabilistic Procrustes mapping strategy combined with joint optimization of geometry and camera poses for unposed 3DGS reconstruction.

Findings

Achieves accurate reconstruction from unposed outdoor images.

Sets new state-of-the-art performance on Waymo and KITTI datasets.

Reconstructs scenes and estimates poses within minutes.

Abstract

3D Gaussian Splatting (3DGS) has emerged as a core technique for 3D representation. Its effectiveness largely depends on precise camera poses and accurate point cloud initialization, which are often derived from pretrained Multi-View Stereo (MVS) models. However, in unposed reconstruction task from hundreds of outdoor images, existing MVS models may struggle with memory limits and lose accuracy as the number of input images grows. To address this limitation, we propose a novel unposed 3DGS reconstruction framework that integrates pretrained MVS priors with the probabilistic Procrustes mapping strategy. The method partitions input images into subsets, maps submaps into a global space, and jointly optimizes geometry and poses with 3DGS. Technically, we formulate the mapping of tens of millions of point clouds as a probabilistic Procrustes problem and solve a closed-form alignment. By employing probabilistic coupling along with a soft dustbin mechanism to reject uncertain correspondences, our method globally aligns point clouds and poses within minutes across hundreds of images. Moreover, we propose a joint optimization framework for 3DGS and camera poses. It constructs Gaussians from confidence-aware anchor points and integrates 3DGS differentiable rendering with an analytical Jacobian to jointly refine scene and poses, enabling accurate reconstruction and pose estimation. Experiments on Waymo and KITTI datasets show that our method achieves accurate reconstruction from unposed image sequences, setting a new state of the art for unposed 3DGS reconstruction.