CLARU
LuMonDepth2026mit

Chang'e-3: An Authentic In-Situ Dataset of the LuMon Benchmark

An authentic, in-situ collection of 168 lunar surface images with metric depth maps and validity masks, designed to evaluate monocular depth estimation networks for extraterrestrial navigation and address the visual domain gap between terrestrial and lunar environments.

Downloads558
Episodes168

Why This Matters for Physical AI

This dataset enables evaluation of monocular depth estimation models under authentic extraterrestrial conditions with extreme visual domain gaps, critical for autonomous lunar navigation and robotics deployment on celestial bodies.

Technical Profile

Modalities
rgbdepth
Environment
outdoorlunar
Task Types
depth-estimation
Episodes
168
Data Format
npy
Annotation Types
depth_mapsvalidity_masks
License
mit
Part of the Chang'e-3: An Authentic In-Situ Dataset of the LuMon Benchmark family

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