{"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/semantic-segmentation-of-sparse-irregular-1","title":"Semantic segmentation of sparse irregular point clouds for leaf/wood discrimination","arxiv_id":"2305.16963","date":"2023-05-26","proceeding":"NeurIPS 2023 11","authors":["Yuchen Bai","Jean-Baptiste Durand","Grégoire Vincent","Florence Forbes"],"abstract":"LiDAR (Light Detection and Ranging) has become an essential part of the remote sensing toolbox used for biosphere monitoring. In particular, LiDAR provides the opportunity to map forest leaf area with unprecedented accuracy, while leaf area has remained an important source of uncertainty affecting models of gas exchanges between the vegetation and the atmosphere. Unmanned Aerial Vehicles (UAV) are easy to mobilize and therefore allow frequent revisits to track the response of vegetation to climate change. However, miniature sensors embarked on UAVs usually provide point clouds of limited density, which are further affected by a strong decrease in density from top to bottom of the canopy due to progressively stronger occlusion. In such a context, discriminating leaf points from wood points presents a significant challenge due in particular to strong class imbalance and spatially irregular sampling intensity. Here we introduce a neural network model based on the Pointnet ++ architecture which makes use of point geometry only (excluding any spectral information). To cope with local data sparsity, we propose an innovative sampling scheme which strives to preserve local important geometric information. We also propose a loss function adapted to the severe class imbalance. We show that our model outperforms state-of-the-art alternatives on UAV point clouds. We discuss future possible improvements, particularly regarding much denser point clouds acquired from below the canopy.","url_abs":"https://arxiv.org/abs/2305.16963v3","url_pdf":"https://arxiv.org/pdf/2305.16963v3.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":"semantic-segmentation-of-sparse-irregular-1","repo_url":"https://github.com/na1an/phd_mission","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/lidar-semantic-segmentation-on-uls-labeled","task":"LIDAR Semantic Segmentation","dataset":"ULS labeled data","model":"SOUL","rank_in_archive_order":1,"of":1,"metrics":{"Binary Accuracy":"0.757","G-mean":"0.744","Specificity":"0.631"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2305.16963","atlas_url":"https://app.syntology.ai/?focus=2305.16963","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.16963"}},"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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/na1an/phd_mission","reach":null}],"summary":{"ran":2,"unverified":1},"by_repo_kind":{"official":{"samples":3,"ran":2,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":3,"samples":[{"code_sha256_prefix":"e728a893952f2603","entry":"PointNetFeaturePropagation","repo":"na1an/phd_mission","repo_kind":"official","path":"src/model.py","file_url":"https://github.com/na1an/phd_mission/blob/HEAD/src/model.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"e728a893952f2603"}},{"code_sha256_prefix":"98fd9eae45a69452","entry":"PointNetSetAbstraction","repo":"na1an/phd_mission","repo_kind":"official","path":"src/model.py","file_url":"https://github.com/na1an/phd_mission/blob/HEAD/src/model.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"98fd9eae45a69452"}},{"code_sha256_prefix":"28623aae319429b8","entry":"Pointnet_plus","repo":"na1an/phd_mission","repo_kind":"official","path":"src/model.py","file_url":"https://github.com/na1an/phd_mission/blob/HEAD/src/model.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"28623aae319429b8"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}