{"url":"/method/panoptic-polarnet","slug":"panoptic-polarnet","name":"Panoptic-PolarNet","full_name":"Panoptic-PolarNet","full_name_withheld":false,"description_markdown":"**Panoptic-PolarNet** is a point cloud segmentation framework for LiDAR point clouds. It learns both semantic segmentation and class-agnostic instance clustering in a single inference network using a polar Bird's Eye View (BEV) representation, enabling the authors to circumvent the issue of occlusion among instances in urban street scenes. We first encode the raw point cloud data with $K$ features into a fixed-size representation on the polar BEV map. Next, we use a single backbone encoder-decoder network to generate semantic prediction, center [heatmap](https://paperswithcode.com/method/heatmap) and offset regression. Finally, we merge these outputs via a voting-based fusion to yield the panoptic segmentation result.","description_state":"present","introduced_year":null,"introduced_by":{"title":null,"paper":null,"first_author":null,"n_authors":0,"url_abs":null,"archive_paper_url":null},"source":{"url":"https://arxiv.org/abs/2103.14962v1","title":"Panoptic-PolarNet: Proposal-free LiDAR Point Cloud Panoptic Segmentation","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"Point Cloud Models","url":"/methods/category/point-cloud-models","pwc_aliases":[]}],"n_papers_tagged":1,"archive_num_papers":null,"papers_newest_first":[{"paper":"/paper/panoptic-polarnet-proposal-free-lidar-point","title":"Panoptic-PolarNet: Proposal-free LiDAR Point Cloud Panoptic Segmentation","date":"2021-03-27","arxiv_id":"2103.14962","n_code_links":2,"syntology":{"ran":6,"of":11,"unverified":5,"pointer_only":0}}],"papers_shown":1,"tasks":[{"task":"/task/clustering","name":"Clustering","papers":1},{"task":"/task/instance-segmentation","name":"Instance Segmentation","papers":1},{"task":"/task/panoptic-segmentation","name":"Panoptic Segmentation","papers":1},{"task":"/task/segmentation","name":"Segmentation","papers":1},{"task":"/task/semantic-segmentation","name":"Semantic Segmentation","papers":1}],"tasks_shown":5,"n_tasks":5,"usage_by_year":[{"year":"2021","papers":1}],"row_source":"embedded","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/panoptic-polarnet"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}