Person-MinkUNet: 3D Person Detection with LiDAR Point Cloud

Bastian Leibe; Dan Jia

Person-MinkUNet: 3D Person Detection with LiDAR Point Cloud

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arxiv 2107.06780 v1 pith:276SOSKO submitted 2021-07-03 cs.CV cs.RO

Dan Jia , Bastian Leibe This is my paper

classification cs.CVcs.RO

keywords detectionpersonnetworkperson-minkunetachievesapplyarchitectureattempt

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In this preliminary work we attempt to apply submanifold sparse convolution to the task of 3D person detection. In particular, we present Person-MinkUNet, a single-stage 3D person detection network based on Minkowski Engine with U-Net architecture. The network achieves a 76.4% average precision (AP) on the JRDB 3D detection benchmark.

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Technical Report for ICRA 2025 GOOSE 3D Semantic Segmentation Challenge: Adaptive Point Cloud Understanding for Heterogeneous Robotic Systems
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Applying Point Prompt Tuning with platform-specific conditioning to a PTv3 backbone improves multi-platform LiDAR segmentation on GOOSE/GOOSE-Ex validation data, with mIoU gains up to 22.59% relative to the PTv3 baseline.

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