Model Zoo
Common settings
- We use distributed training.
- For fair comparison with other codebases, we report the GPU memory as the maximum value of
torch.cuda.max_memory_allocated()
for all 8 GPUs. Note that this value is usually less than whatnvidia-smi
shows. - We report the inference time as the total time of network forwarding and post-processing, excluding the data loading time. Results are obtained with the script benchmark.py which computes the average time on 2000 images.
Baselines
SECOND
Please refer to SECOND for details. We provide SECOND baselines on KITTI and Waymo datasets.
PointPillars
Please refer to PointPillars for details. We provide pointpillars baselines on KITTI, nuScenes, Lyft, and Waymo datasets.
Part-A2
Please refer to Part-A2 for details.
VoteNet
Please refer to VoteNet for details. We provide VoteNet baselines on ScanNet and SUNRGBD datasets.
Dynamic Voxelization
Please refer to Dynamic Voxelization for details.
MVXNet
Please refer to MVXNet for details.
RegNetX
Please refer to RegNet for details. We provide pointpillars baselines with RegNetX backbones on nuScenes and Lyft datasets currently.
nuImages
We also support baseline models on nuImages dataset. Please refer to nuImages for details. We report Mask R-CNN, Cascade Mask R-CNN and HTC results currently.
H3DNet
Please refer to H3DNet for details.
3DSSD
Please refer to 3DSSD for details.
CenterPoint
Please refer to CenterPoint for details.
SSN
Please refer to SSN for details. We provide pointpillars with shape-aware grouping heads used in SSN on the nuScenes and Lyft datasets currently.
ImVoteNet
Please refer to ImVoteNet for details. We provide ImVoteNet baselines on SUNRGBD dataset.
FCOS3D
Please refer to FCOS3D for details. We provide FCOS3D baselines on the nuScenes dataset.
PointNet++
Please refer to PointNet++ for details. We provide PointNet++ baselines on ScanNet and S3DIS datasets.
Group-Free-3D
Please refer to Group-Free-3D for details. We provide Group-Free-3D baselines on ScanNet dataset.
ImVoxelNet
Please refer to ImVoxelNet for details. We provide ImVoxelNet baselines on KITTI dataset.
PAConv
Please refer to PAConv for details. We provide PAConv baselines on S3DIS dataset.
DGCNN
Please refer to DGCNN for details. We provide DGCNN baselines on S3DIS dataset.
SMOKE
Please refer to SMOKE for details. We provide SMOKE baselines on KITTI dataset.
PGD
Please refer to PGD for details. We provide PGD baselines on KITTI and nuScenes dataset.
PointRCNN
Please refer to PointRCNN for details. We provide PointRCNN baselines on KITTI dataset.
MonoFlex
Please refer to MonoFlex for details. We provide MonoFlex baselines on KITTI dataset.
SA-SSD
Please refer to SA-SSD for details. We provide SA-SSD baselines on the KITTI dataset.
FCAF3D
Please refer to FCAF3D for details. We provide FCAF3D baselines on the ScanNet, S3DIS, and SUN RGB-D datasets.
PV-RCNN
Please refer to PV-RCNN for details. We provide PV-RCNN baselines on the KITTI dataset.
BEVFusion
Please refer to BEVFusion for details. We provide BEVFusion baselines on the NuScenes dataset.
CenterFormer
Please refer to CenterFormer for details. We provide CenterFormer baselines on the Waymo dataset.
TR3D
Please refer to TR3D for details. We provide TR3D baselines on the ScanNet, SUN RGB-D and S3DIS dataset.
DETR3D
Please refer to DETR3D for details. We provide DETR3D baselines on the nuScenes dataset.
PETR
Please refer to PETR for details. We provide PETR baselines on the nuScenes dataset.
TPVFormer
Please refer to TPVFormer for details. We provide TPVFormer baselines on the nuScenes dataset.
Mixed Precision (FP16) Training
Please refer to Mixed Precision (FP16) Training on PointPillars for details.