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3dssd_4x4_kitti-3d-car.py | 2 years ago | |
README.md | 2 years ago | |
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We implement 3DSSD and provide the results and checkpoints on KITTI datasets.
@inproceedings{yang20203dssd,
author = {Zetong Yang and Yanan Sun and Shu Liu and Jiaya Jia},
title = {3DSSD: Point-based 3D Single Stage Object Detector},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2020}
}
Some settings in our implementation are different from the official implementation, which bring marginal differences to the performance on KITTI datasets in our experiments. To simplify and unify the models of our implementation, we skip them in our models. These differences are listed as below:
batch normalization
during training.DataBaseSampler
for data augmentation, the official code uses road planes as reference to place the sampled objects while we do not.Backbone | Class | Lr schd | Mem (GB) | Inf time (fps) | mAP | Download |
---|---|---|---|---|---|---|
PointNet2SAMSG | Car | 72e | 4.7 | 78.69(81.27)1 | model | log |
[1]: We report two different 3D object detection performance here. 78.69mAP is evaluated by our evaluation code and 81.27mAP is evaluated by the official development kit (so as that used in the paper and official code of 3DSSD ). We found that the commonly used Python implementation of rotate_iou
which is used in our KITTI dataset evaluation, is different from the official implemention in KITTI benchmark.
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