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Introduction

We provide scripts for multi-modality/single-modality (LiDAR-based/vision-based), indoor/outdoor 3D detection and 3D semantic segmentation demos. The pre-trained models can be downloaded from model zoo. We provide pre-processed sample data from KITTI, SUN RGB-D, nuScenes and ScanNet dataset. You can use any other data following our pre-processing steps.

Testing

3D Detection

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}] [--show]

The visualization results including a point cloud and predicted 3D bounding boxes will be saved in ${OUT_DIR}/PCD_NAME, which you can open using MeshLab. Note that if you set the flag --show, the prediction result will be displayed online using Open3D.

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}] [--show]

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 ${OUT_DIR}/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

Monocular 3D Detection

To test a monocular 3D detector on image data, simply run:

python demo/mono_det_demo.py ${IMAGE_FILE} ${ANNOTATION_FILE} ${CONFIG_FILE} ${CHECKPOINT_FILE} [--device ${GPU_ID}] [--out-dir ${OUT_DIR}] [--show]

where the ANNOTATION_FILE should provide the 3D to 2D projection matrix (camera intrinsic matrix). The visualization results including an image and its predicted 3D bounding boxes projected on the image will be saved in ${OUT_DIR}/PCD_NAME.

Example on nuScenes data using FCOS3D model:

python demo/mono_det_demo.py demo/data/nuscenes/n015-2018-07-24-11-22-45+0800__CAM_BACK__1532402927637525.jpg demo/data/nuscenes/n015-2018-07-24-11-22-45+0800__CAM_BACK__1532402927637525_mono3d.coco.json configs/fcos3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_finetune.py checkpoints/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_finetune_20210717_095645-8d806dc2.pth

Note that when visualizing results of monocular 3D detection for flipped images, the camera intrinsic matrix should also be modified accordingly. See more details and examples in PR #744.

3D Segmentation

To test a 3D segmentor on point cloud data, simply run:

python demo/pc_seg_demo.py ${PCD_FILE} ${CONFIG_FILE} ${CHECKPOINT_FILE} [--device ${GPU_ID}] [--out-dir ${OUT_DIR}] [--show]

The visualization results including a point cloud and its predicted 3D segmentation mask will be saved in ${OUT_DIR}/PCD_NAME.

Example on ScanNet data using PointNet++ (SSG) model:

python demo/pc_seg_demo.py demo/data/scannet/scene0000_00.bin configs/pointnet2/pointnet2_ssg_16x2_cosine_200e_scannet_seg-3d-20class.py checkpoints/pointnet2_ssg_16x2_cosine_200e_scannet_seg-3d-20class_20210514_143644-ee73704a.pth
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