We implement H3DNet and provide the result and checkpoints on ScanNet datasets.
@inproceedings{zhang2020h3dnet,
author = {Zhang, Zaiwei and Sun, Bo and Yang, Haitao and Huang, Qixing},
title = {H3DNet: 3D Object Detection Using Hybrid Geometric Primitives},
booktitle = {Proceedings of the European Conference on Computer Vision},
year = {2020}
}
Backbone | Lr schd | Mem (GB) | Inf time (fps) | [email protected] | [email protected] | Download |
---|---|---|---|---|---|---|
MultiBackbone | 3x | 7.9 | 66.43 | 48.01 | model | log |
Notice: If your current mmdetection3d version >= 0.6.0, and you are using the checkpoints downloaded from the above links or using checkpoints trained with mmdetection3d version < 0.6.0, the checkpoints have to be first converted via tools/model_converters/convert_h3dnet_checkpoints.py:
python ./tools/model_converters/convert_h3dnet_checkpoints.py ${ORIGINAL_CHECKPOINT_PATH} --out=${NEW_CHECKPOINT_PATH}
Then you can use the converted checkpoints following getting_started.md.