[2608.14428] GhostPoint: Self-Supervised Representation Learning by Hallucinating Occluded LiDAR Structure Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Vision and Pattern Recognition arXiv:2608.14428 (cs) [Submitted on 14 Aug 2026] Title: GhostPoint: Self-Supervised Representation Learning by Hallucinating Occluded LiDAR Structure Authors: Mohamed Abdelsamad , Bin Yang , Michael Ulrich , Miao Zhang , Yakov Miron , Alexandru Paul Condurache , Abhinav Valada View a PDF of the paper titled GhostPoint: Self-Supervised Representation Learning by Hallucinating Occluded LiDAR Structure, by Mohamed Abdelsamad and 6 other authors View PDF HTML (experimental) Abstract: 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 in local neighborhoods around discovered instances, generated via a novel instance voxel dilation. In GhostPoint, an encoder processes observed returns, and an additional predictor infers neighborhood representations from observed context. In addition to standard encoder-level supervision, we introduce a predictor-level supervision scheme on sampled voxels from generated neighborhoods. Specifically, observed (visible/masked) voxels match teacher-encoder targets, while unobserved voxels match teacher-predictor hallucinations. This design encourages the learned representation to explicitly model structure beyond observed returns. Extensive evaluations on nuScenes and Waymo demonstrate that our method achieves state-of-the-art performance, consistently improving downstream 3D detection, especially under sparse scans and limited labels. Comments: Accepted by ECCV2026 Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2608.14428 [cs.CV] (or arXiv:2608.14428v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2608.14428 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Bin Yang [ view email ] [v1] Fri, 14 Aug 2026 16:10:35 UTC (14,499 KB) Full-text links: Access Paper: View a PDF of the paper titled GhostPoint: Self-Supervised Representation Learning by Hallucinating Occluded LiDAR Structure, by Mohamed Abdelsamad and 6 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2026-08 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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