Demo¶
Introduction¶
We provide scipts for multi-modality/single-modality and indoor/outdoor 3D detection demos. The pre-trained models can be downloaded from model zoo. We provide pre-processed sample data from KITTI and SUN RGB-D dataset. You can use any other data following our pre-processing steps.
Testing¶
Single-modality demo¶
To test a 3D detector on point cloud data, simply run:
python demo/pcd_demo.py ${PCD_FILE} ${CONFIG_FILE} ${CHECKPOINT_FILE} [--device ${GPU_ID}] [--score-thr ${SCORE_THR}] [--out-dir ${OUT_DIR}]
The visualization results including a point cloud and predicted 3D bounding boxes will be saved in demo/PCD_NAME
, which you can open using MeshLab.
Example on KITTI data using SECOND model:
python demo/pcd_demo.py demo/data/kitti/kitti_000008.bin configs/second/hv_second_secfpn_6x8_80e_kitti-3d-car.py checkpoints/hv_second_secfpn_6x8_80e_kitti-3d-car_20200620_230238-393f000c.pth
Example on SUN RGB-D data using VoteNet model:
python demo/pcd_demo.py demo/data/sunrgbd/sunrgbd_000017.bin configs/votenet/votenet_16x8_sunrgbd-3d-10class.py checkpoints/votenet_16x8_sunrgbd-3d-10class_20200620_230238-4483c0c0.pth
Remember to convert the VoteNet checkpoint if you are using mmdetection3d version >= 0.6.0. See its README for detailed instructions on how to convert the checkpoint.
Multi-modality demo¶
To test a 3D detector on multi-modality data (typically point cloud and image), simply run:
python demo/multi_modality_demo.py ${PCD_FILE} ${IMAGE_FILE} ${ANNOTATION_FILE} ${CONFIG_FILE} ${CHECKPOINT_FILE} [--device ${GPU_ID}] [--score-thr ${SCORE_THR}] [--out-dir ${OUT_DIR}]
where the ANNOTATION_FILE
should provide the 3D to 2D projection matrix. The visualization results including a point cloud, an image, predicted 3D bounding boxes and their projection on the image will be saved in demo/PCD_NAME
.
Example on KITTI data using MVX-Net model:
python demo/multi_modality_demo.py demo/data/kitti/kitti_000008.bin demo/data/kitti/kitti_000008.png demo/data/kitti/kitti_000008_infos.pkl configs/mvxnet/dv_mvx-fpn_second_secfpn_adamw_2x8_80e_kitti-3d-3class.py checkpoints/dv_mvx-fpn_second_secfpn_adamw_2x8_80e_kitti-3d-3class_20200621_003904-10140f2d.pth
Example on SUN RGB-D data using ImVoteNet model:
python demo/multi_modality_demo.py demo/data/sunrgbd/sunrgbd_000017.bin demo/data/sunrgbd/sunrgbd_000017.jpg demo/data/sunrgbd/sunrgbd_000017_infos.pkl configs/imvotenet/imvotenet_stage2_16x8_sunrgbd-3d-10class.py checkpoints/imvotenet_stage2_16x8_sunrgbd-3d-10class_20210323_184021-d44dcb66.pth