Semantic Segmentation and Depth Estimation for Real-Time Lunar Surface Mapping Using 3D Gaussian Splatting

TL;DR

This paper introduces a real-time lunar surface mapping framework that combines semantic segmentation, dense depth estimation, and 3D Gaussian Splatting to produce detailed, large-scale maps suitable for lunar exploration.

Contribution

It presents a novel integration of perception models with 3D Gaussian Splatting for efficient, high-accuracy lunar surface mapping in challenging conditions.

Findings

Achieved 3 cm geometric height accuracy over 120 meters

Demonstrated real-time performance with dense perception models

Enabled effective view synthesis and SLAM foundation

Abstract

Navigation and mapping on the lunar surface require robust perception under challenging conditions, including poorly textured environments, high-contrast lighting, and limited computational resources. This paper presents a real-time mapping framework that integrates dense perception models with a 3D Gaussian Splatting (3DGS) representation. We first benchmark several models on synthetic datasets generated with the LuPNT simulator, selecting a stereo dense depth estimation model based on Gated Recurrent Units for its balance of speed and accuracy in depth estimation, and a convolutional neural network for its superior performance in detecting semantic segments. Using ground truth poses to decouple the local scene understanding from the global state estimation, our pipeline reconstructs a 120-meter traverse with a geometric height accuracy of approximately 3 cm, outperforming a traditional point cloud baseline without LiDAR. The resulting 3DGS map enables novel view synthesis and serves as a foundation for a full SLAM system, where its capacity for joint map and pose optimization would offer significant advantages. Our results demonstrate that combining semantic segmentation and dense depth estimation with learned map representations is an effective approach for creating detailed, large-scale maps to support future lunar surface missions.