README.md

Deep Hough Voting for 3D Object Detection in Point Clouds

Introduction

We implement VoteNet and provide the result and checkpoints on ScanNet and SUNRGBD datasets.

@inproceedings{qi2019deep,
    author = {Qi, Charles R and Litany, Or and He, Kaiming and Guibas, Leonidas J},
    title = {Deep Hough Voting for 3D Object Detection in Point Clouds},
    booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
    year = {2019}
}

Results

ScanNet

Backbone	Lr schd	Mem (GB)	Inf time (fps)	AP@0.25	AP@0.5	Download
PointNet++	3x	4.1		62.90	39.91	model | log

SUNRGBD

Backbone	Lr schd	Mem (GB)	Inf time (fps)	AP@0.25	AP@0.5	Download
PointNet++	3x	8.1		59.07	35.77	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_votenet_checkpoints.py:

python ./tools/model_converters/convert_votenet_checkpoints.py ${ORIGINAL_CHECKPOINT_PATH} --out=${NEW_CHECKPOINT_PATH}

Then you can use the converted checkpoints following getting_started.md.

Indeterminism

Since test data preparation randomly downsamples the points, and the test script uses fixed random seeds while the random seeds of validation in training are not fixed, the test results may be slightly different from the results reported above.

IoU loss

Adding IoU loss (simply = 1-IoU) boosts VoteNet's performance. To use IoU loss, add this loss term to the config file:

iou_loss=dict(type='AxisAlignedIoULoss', reduction='sum', loss_weight=10.0 / 3.0)

Backbone	Lr schd	Mem (GB)	Inf time (fps)	AP@0.25	AP@0.5	Download
PointNet++	3x	4.1		63.81	44.21	/

For now, we only support calculating IoU loss for axis-aligned bounding boxes since the CUDA op of general 3D IoU calculation does not implement the backward method. Therefore, IoU loss can only be used for ScanNet dataset for now.