3Dガウススプラッティングを用いたリアルタイム月面マッピングのためのセマンティックセグメンテーションと深度推定
Semantic Segmentation and Depth Estimation for Real-Time Lunar Surface Mapping Using 3D Gaussian Splatting
月面探査向けに、セマンティックセグメンテーションと深度推定を統合した3Dガウススプラッティングベースのリアルタイムマッピング手法を提案し、シミュレーションデータで精度を実証した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Guillem Casadesus Vila, Adam Dai, Grace Gao
分類: cs.CV, cs.RO
原文アブストラクト
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.
