DINO-SLAM: DINO-Informed RGB-D SLAM for Neural Implicit and Explicit Representations

This paper presents DINO-SLAM, a DINO-informed designstrategy to enhance implicit (Neural Radiance Field – NeRF) and explicitrepresentations (Gaussian Splatting – GS) in SLAM systems through themore comprehensive semantic understanding enabled by DINO. This lat-ter alone, however, lacks proper 3D geometry understanding, allowingonly for marginal improvements. Therefore, we rely on a Scene Geom-etry Encoder (SGE) to lift DINO features into geometry-aware DINOfeatures (geoDINO), to better understand those geometric relationshipsthat vanilla DINO features fail to capture. Building upon it, we pro-pose two foundational paradigms for NeRF and GS SLAM systems in-tegrating geoDINO features. Compared to state-of-the-art methods, ourDINO-informed pipelines achieve superior performance on the Replica,ScanNet, and TUM datasets.