GhostPoint: Self-Supervised Representation Learning by Hallucinating Occluded LiDAR Structure
3D object detection from LiDAR point clouds is a core problem in autonomous driving. Recent advances in self-supervised learning (SSL) enable scalable pretraining and transfers well to per-point tasks such as semantic and panoptic segmentation, but transfer to 3D detection remains weaker. We analyze recent SSL methods and find that most objectives are defined only on measured LiDAR returns from visible surfaces, leaving occluded and unobserved regions unconstrained. This visible-surface bias can be sufficient for point-wise prediction, but 3D detection requires robustness to missing structure. To address this gap, we propose GhostPoint, an SSL framework that hallucinates latent features i...
| Authors | Mohamed Abdelsamad, Bin Yang, Michael Ulrich et al. |
|---|---|
| Primary category | cs.CV |
| Published | 14 August 2026 |
| Last updated at source | 14 August 2026 |
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