Vernata: Self-Supervised Learning of LiDAR Point Representations

Oliver Lemke; Alexander Liniger; Abel Gawel; Marco Hutter

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arXiv · 2608.06919

Vernata: Self-Supervised Learning of LiDAR Point Representations

Oliver Lemke·Alexander Liniger·Abel Gawel·Marco Hutter

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Abstract

LiDAR serves as a primary sensing modality for robots operating in outdoor environments. However, the performance of deep learning models in this domain is severely limited by the scarcity of labeled data, a direct result of the high cost of 3D annotation. Self-supervised learning addresses this scarcity by learning general-purpose features from unlabeled data. In this work, we present a multi-modal, multi-teacher distillation framework for self-supervised learning on outdoor LiDAR point clouds. Building upon the Sonata architecture, we introduce Vernata, consisting of three extensions: sparse view augmentation to improve robustness against varying point densities, a memory bank mechanism to stabilize resource-constrained training, and cross-modal distillation utilizing dense, high-resolution 2D image features to enable fine-grained semantic guidance. We evaluate our method on the GrandTour, TartanGround, and Waymo datasets, as well as data collected from our own robotic platforms. Our experiments demonstrate a significant performance improvement over Sonata baselines, yielding mIoU scores of 54.7 on TartanGround (+5.9 points, +12.1%) and 57.1 on Waymo (+7.3 points, +14.7%). Finally, we show that the self-supervised approach maintains strong performance even in reduced-modality settings (lacking color or normals), achieving competitive mIoU scores of 49.4 and 50.2 on the respective datasets.

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Oliver Lemke, Alexander Liniger, Abel Gawel, Marco Hutter. 2026-08-07. Vernata: Self-Supervised Learning of LiDAR Point Representations. https://arxiv.org/abs/2608.06919

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