Awesome
Better Call SAL: Towards Learning to Segment Anything in Lidar
Accepted to European Conference on Computer Vision (ECCV), 2024
Code coming soon!
Authors: Aljosa Osep* Tim Meinhardt* Francesco Ferroni,Neehar Peri, Deva Ramanan and Laura Leal-Taixe (* Equal Contribution)
Summary: Our SAL (Segment Anything in Lidar) method consists of a text-promptable zero-shot model for segmenting and classifying any object in Lidar, and a pseudo-labeling engine that facilitates model training without manual supervision. Our pseudo-labels consist of instance masks and corresponding CLIP tokens, which we lift to Lidar using calibrated multi-modal data. By training our model on these labels, we distill the 2D foundation models into our Lidar SAL model. Even without manual labels, our model achieves 91% in terms of class-agnostic segmentation and 44% in terms of zero-shot LPS of the fully supervised state-of-the-art. Moreover, we show that SAL supports arbitrary class prompts, can be easily extended to new datasets, and shows significant potential to improve with increasing amounts of self-labeled data.
Segment Anything in Lidar (SAL): The SAL model performs class-agnostic instance segmentation (i) and zero-shot classification via text prompting. This allows us to not only predict semantic/panoptic segmentation (ii) for fixed class vocabularies but segment any object (iii and iv) in a given Lidar scan.
SAL overview: Given a Lidar scan and a class vocabulary prompt, specified as a list of per-class free-form text descriptions (left), SAL segments and classifies objects (thing and stuff classes). As labeled data for training such a model does not exist, we supervise SAL by distilling off-the-shelf vision foundation models to Lidar (right).