Vernata: LiDAR点群表現の自己教師あり学習
Vernata: Self-Supervised Learning of LiDAR Point Representations
屋外LiDAR点群のための自己教師あり学習フレームワークVernataを提案。マルチモーダル・マルチティーチャー蒸留により、ラベルなしデータから汎用特徴を学習し、セグメンテーション性能を大幅に向上させた。
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著者: Oliver Lemke, Alexander Liniger, Abel Gawel, Marco Hutter
分類: cs.CV, cs.RO
原文アブストラクト
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.
