TUN3D: Towards Real-World Scene Understanding from Unposed Images
M:3L co-authored TUN3D, accepted at ICRA 2026, an A*-ranked conference. It is the first system to jointly estimate room layouts and detect 3D objects in real scans from multi-view images without ground-truth camera poses or depth supervision. A lightweight sparse-convolutional backbone feeds dedicated detection and parametric wall-layout heads, enabling one model to work with point clouds, posed images, or fully unposed RGB captures and substantially advancing holistic indoor-scene understanding.
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