{"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/4d-stop-panoptic-segmentation-of-4d-lidar","title":"4D-StOP: Panoptic Segmentation of 4D LiDAR using Spatio-temporal Object Proposal Generation and Aggregation","arxiv_id":"2209.14858","date":"2022-09-29","proceeding":null,"authors":["Lars Kreuzberg","Idil Esen Zulfikar","Sabarinath Mahadevan","Francis Engelmann","Bastian Leibe"],"abstract":"In this work, we present a new paradigm, called 4D-StOP, to tackle the task of 4D Panoptic LiDAR Segmentation. 4D-StOP first generates spatio-temporal proposals using voting-based center predictions, where each point in the 4D volume votes for a corresponding center. These tracklet proposals are further aggregated using learned geometric features. The tracklet aggregation method effectively generates a video-level 4D scene representation over the entire space-time volume. This is in contrast to existing end-to-end trainable state-of-the-art approaches which use spatio-temporal embeddings that are represented by Gaussian probability distributions. Our voting-based tracklet generation method followed by geometric feature-based aggregation generates significantly improved panoptic LiDAR segmentation quality when compared to modeling the entire 4D volume using Gaussian probability distributions. 4D-StOP achieves a new state-of-the-art when applied to the SemanticKITTI test dataset with a score of 63.9 LSTQ, which is a large (+7%) improvement compared to current best-performing end-to-end trainable methods. The code and pre-trained models are available at: https://github.com/LarsKreuzberg/4D-StOP.","url_abs":"https://arxiv.org/abs/2209.14858v1","url_pdf":"https://arxiv.org/pdf/2209.14858v1.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":[{"paper_slug":"4d-stop-panoptic-segmentation-of-4d-lidar","repo_url":"https://github.com/larskreuzberg/4d-stop","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"4d-panoptic-segmentation","task_name":"4D Panoptic Segmentation"},{"task_slug":"object-proposal-generation","task_name":"Object Proposal Generation"},{"task_slug":"panoptic-segmentation","task_name":"Panoptic Segmentation"}],"methods":[{"method_slug":"test","method_name":"Test"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/4d-panoptic-segmentation-on-semantickitti","task":"4D Panoptic Segmentation","dataset":"SemanticKITTI","model":"4D-StOP","rank_in_archive_order":4,"of":7,"metrics":{"LSTQ":"63.9"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2209.14858","atlas_url":"https://app.syntology.ai/?focus=2209.14858","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.14858"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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