Sample, crop, track: self-supervised mobile 3D object detectionfor urban driving LiDAR
Deep learning has led to great progress in the detection of mobile (i.e. movement-capable) objects in urban driving scenes in recent years. Supervised approaches typically require the annotation of large training sets; there has thus been great interest in leveraging weakly, semi- or selfsupervised...
| Main Authors: | , , , , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
IEEE
2023
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