{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/360-circ-from-a-single-camera-a-few-shot","title":"360$^\\circ$ from a Single Camera: A Few-Shot Approach for LiDAR Segmentation","arxiv_id":"2309.06197","date":"2023-09-12","proceeding":null,"authors":["Laurenz Reichardt","Nikolas Ebert","Oliver Wasenmüller"],"abstract":"Deep learning applications on LiDAR data suffer from a strong domain gap when applied to different sensors or tasks. In order for these methods to obtain similar accuracy on different data in comparison to values reported on public benchmarks, a large scale annotated dataset is necessary. However, in practical applications labeled data is costly and time consuming to obtain. Such factors have triggered various research in label-efficient methods, but a large gap remains to their fully-supervised counterparts. Thus, we propose ImageTo360, an effective and streamlined few-shot approach to label-efficient LiDAR segmentation. Our method utilizes an image teacher network to generate semantic predictions for LiDAR data within a single camera view. The teacher is used to pretrain the LiDAR segmentation student network, prior to optional fine-tuning on 360$^\\circ$ data. Our method is implemented in a modular manner on the point level and as such is generalizable to different architectures. We improve over the current state-of-the-art results for label-efficient methods and even surpass some traditional fully-supervised segmentation networks.","url_abs":"https://arxiv.org/abs/2309.06197v1","url_pdf":"https://arxiv.org/pdf/2309.06197v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semi-supervised-semantic-segmentation","task_name":"Semi-Supervised Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/semi-supervised-semantic-segmentation-on-24","task":"Semi-Supervised Semantic Segmentation","dataset":"SemanticKITTI","model":"360° from a Single Camera: A Few-Shot Approach for LiDAR Segmentation (All)","rank_in_archive_order":4,"of":12,"metrics":{"mIOU (1% Test set)":"57.7","mIoU (1% Labels)":"59.5","mIoU (10% Labels)":"62.4","mIoU (20% Labels)":"64.2","mIoU (50% Labels)":"66.1"},"uses_additional_data":true}],"syntology":{"syntology_url":"https://syntology.ai/paper/2309.06197","atlas_url":"https://app.syntology.ai/?focus=2309.06197","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}