AI RESEARCH
3D sans 3D Scans: Scalable Pre-training from Video-Generated Point Clouds
arXiv CS.CV
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ArXi:2512.23042v2 Announce Type: replace Despite recent progress in 3D self-supervised learning, collecting large-scale 3D scene scans remains expensive and labor-intensive. In this work, we investigate whether 3D representations can be learned from unlabeled videos recorded without any real 3D sensors. We present Laplacian-Aware Multi-level 3D Clustering with Sinkhorn-Knopp (LAM3C), a self-supervised framework that learns from video-generated point clouds reconstructed from unlabeled videos. We first.