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SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics

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arxiv 2403.06501 v2 pith:NMIRFOO3 submitted 2024-03-11 cs.CV

Hayeon O , Chanuk Yang , Kunsoo Huh This is my paper · ORCID
classification cs.CV
keywords objectdetectionsesameexistinginformationmethodsemanticaddresses
verification ladder T0 review T1 audit T2 compute T3 formal
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In autonomous driving, 3D object detection provides more precise information for downstream tasks, including path planning and motion estimation, compared to 2D object detection. In this paper, we propose SeSame: a method aimed at enhancing semantic information in existing LiDAR-only based 3D object detection. This addresses the limitation of existing 3D detectors, which primarily focus on object presence and classification, thus lacking in capturing relationships between elemental units that constitute the data, akin to semantic segmentation. Experiments demonstrate the effectiveness of our method with performance improvements on the KITTI object detection benchmark. Our code is available at https://github.com/HAMA-DL-dev/SeSame

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