Inference
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
Point cloud 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 PointPillars model:
python demo/pcd_demo.py demo/data/kitti/000008.bin configs/pointpillars/pointpillars_hv_secfpn_8xb6-160e_kitti-3d-car.py ${CHECKPOINT_FILE} --show
Example on SUN RGB-D data using VoteNet model:
python demo/pcd_demo.py demo/data/sunrgbd/sunrgbd_000017.bin configs/votenet/votenet_8xb16_sunrgbd-3d.py ${CHECKPOINT_FILE} --show
Monocular 3D demo
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}] [--cam-type ${CAM_TYPE}] [--score-thr ${SCORE-THR}] [--out-dir ${OUT_DIR}] [--show]
where the ANNOTATION_FILE should provide the 3D to 2D projection matrix (camera intrinsic matrix), and CAM_TYPE should be specified according to dataset. For example, if you want to inference on the front camera image, the CAM_TYPE should be set as CAM_2 for KITTI, and CAM_FRONT for nuScenes. By specifying CAM_TYPE, you can even infer on any camera images for datasets with multi-view cameras, such as nuScenes and Waymo. SCORE-THR is the 3D bbox threshold while visualization. The visualization results including an image and its predicted 3D bounding boxes projected on the image will be saved in ${OUT_DIR}/IMG_NAME.
Example on KITTI data using PGD model:
python demo/mono_det_demo.py demo/data/kitti/000008.png demo/data/kitti/000008.pkl configs/pgd/pgd_r101-caffe_fpn_head-gn_4xb3-4x_kitti-mono3d.py ${CHECKPOINT_FILE} --show --cam-type CAM2 --score-thr 8
Note: For PGD, the prediction score is not among (0, 1).
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.pkl configs/fcos3d/fcos3d_r101-caffe-dcn_fpn_head-gn_8xb2-1x_nus-mono3d_finetune.py ${CHECKPOINT_FILE} --show --cam-type CAM_BACK
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.
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/000008.bin demo/data/kitti/000008.png demo/data/kitti/000008.pkl configs/mvxnet/mvxnet_fpn_dv_second_secfpn_8xb2-80e_kitti-3d-3class.py ${CHECKPOINT_FILE} --cam-type CAM2 --show
Example on SUN RGB-D data using ImVoteNet model:
python demo/multi_modality_demo.py demo/data/sunrgbd/000017.bin demo/data/sunrgbd/000017.jpg demo/data/sunrgbd/sunrgbd_000017_infos.pkl configs/imvotenet/imvotenet_stage2_8xb16_sunrgbd-3d.py ${CHECKPOINT_FILE} --cam-type CAM0 --show --score-thr 0.6
Example on NuScenes data using BEVFusion model:
python demo/multi_modality_demo.py demo/data/nuscenes/n015-2018-07-24-11-22-45+0800__LIDAR_TOP__1532402927647951.pcd.bin demo/data/nuscenes/ demo/data/nuscenes/n015-2018-07-24-11-22-45+0800.pkl projects/BEVFusion/configs/bevfusion_voxel0075_second_secfpn_8xb4-cyclic-20e_nus-3d.py ${CHECKPOINT_FILE} --cam-type all --score-thr 0.2 --show
3D Segmentation
To test a 3D segmentor on point cloud data, simply run:
python demo/pcd_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/pcd_seg_demo.py demo/data/scannet/scene0000_00.bin configs/pointnet2/pointnet2_ssg_2xb16-cosine-200e_scannet-seg.py ${CHECKPOINT_FILE} --show