PyTorch code release of the paper "Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D Object Detection",
by Deepti Hegde, Vishal M. Patel
(Currently has instructions for model inference and evaluation only, training steps to be updated soon.)
Follow the instructions for installation and implementation of the method for each base object detection network in the respective folders SECOND-iou and PointRCNN
-
Organize each folder inside data like the following
AttentivePrototypeSFUDA
├── data (main data folder)
│ ├── kitti
│ │ │── ImageSets
│ │ │── training
│ │ │ ├──calib & velodyne & label_2 & image_2 & (optional: planes)
│ │ │── testing
│ │ │ ├──calib & velodyne & image_2
|
|
│ ├── nuscenes
│ │ │── v1.0-trainval (or v1.0-mini if you use mini)
│ │ │ │── samples
│ │ │ │── sweeps
│ │ │ │── maps
│ │ │ │── v1.0-trainval
|
|
│ ├── waymo
│ │ │── ImageSets
│ │ │── raw_data
│ │ │ │── segment-xxxxxxxx.tfrecord
| | | |── ...
| | |── waymo_processed_data
│ │ │ │── segment-xxxxxxxx/
| | | |── ...
│ │ │── pcdet_gt_database_train_sampled_xx/
│ │ │── pcdet_waymo_dbinfos_train_sampled_xx.pkl
|
|
├── PointRCNN
| ├── data (link to main data folder)
| ├── pointrcnn_attention
├── SECOND-iou
| ├── data (link to main data folder)
| ├── pcdet
| ├── tools
We implement the proposed method for two object detectors, SECOND-iou and PointRCNN for several domain shift scenarios. You can find the folder of pretrained models here. Find specific model downloads and their corresponding config files below.
| Domain shift | Model file | Configuration file |
|---|---|---|
| Waymo -> KITTI | download | link |
| Waymo -> nuScenes | download | link |
| nuScenes -> KITTI | download | link |
| Domain shift | Model file | Configuration file |
|---|---|---|
| Waymo -> KITTI | download | link |
| KITTI -> nuScenes | download | link |
| nuScenes -> KITTI | download | link |
Follow the instructions to implement the method in the folders SECOND-iou and PointRCNN
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent