ES-Gaussian: Gaussian Splatting Mapping via Error Space-Based Gaussian Completion

TL;DR

ES-Gaussian is a novel indoor 3D reconstruction system that combines low-cost sensors with error correction techniques to produce high-quality models suitable for robot navigation.

Contribution

The paper introduces ES-Gaussian, a system that integrates Visual Error Construction and a new 3DGS initialization method for improved resource-efficient indoor mapping.

Findings

Outperforms existing methods on Dreame-SR and public datasets.

Effectively reconstructs high-quality 3D models with sparse data.

Demonstrates robustness in resource-constrained environments.

Abstract

Accurate and affordable indoor 3D reconstruction is critical for effective robot navigation and interaction. Traditional LiDAR-based mapping provides high precision but is costly, heavy, and power-intensive, with limited ability for novel view rendering. Vision-based mapping, while cost-effective and capable of capturing visual data, often struggles with high-quality 3D reconstruction due to sparse point clouds. We propose ES-Gaussian, an end-to-end system using a low-altitude camera and single-line LiDAR for high-quality 3D indoor reconstruction. Our system features Visual Error Construction (VEC) to enhance sparse point clouds by identifying and correcting areas with insufficient geometric detail from 2D error maps. Additionally, we introduce a novel 3DGS initialization method guided by single-line LiDAR, overcoming the limitations of traditional multi-view setups and enabling effective reconstruction in resource-constrained environments. Extensive experimental results on our new Dreame-SR dataset and a publicly available dataset demonstrate that ES-Gaussian outperforms existing methods, particularly in challenging scenarios. The project page is available at https://chenlu-china.github.io/ES-Gaussian/.