PointInst3D: Segmenting 3D Instances by Points
نویسندگان
چکیده
The current state-of-the-art methods in 3D instance segmentation typically involve a clustering step, despite the tendency towards heuristics, greedy algorithms, and lack of robustness to changes data statistics. In contrast, we propose fully-convolutional point cloud method that works per-point prediction fashion. doing so it avoids challenges clustering-based face: introducing dependencies among different tasks model. We find key its success is assigning suitable target each sampled point. Instead commonly used static or distance-based assignment strategies, use an Optimal Transport approach optimally assign masks points according dynamic matching costs. Our achieves promising results on both ScanNet S3DIS benchmarks. proposed removes inter-task thus represents simpler more flexible framework than other competing methods, while achieving improved accuracy.
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ژورنال
عنوان ژورنال: Lecture Notes in Computer Science
سال: 2022
ISSN: ['1611-3349', '0302-9743']
DOI: https://doi.org/10.1007/978-3-031-20062-5_17