Official implementation for the paper "Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D Object Detection"

Related tags

Deep LearningAttentivePrototypeSFUDA
Overview

Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D Object Detection

PyTorch code release of the paper "Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D Object Detection",

by Deepti Hegde, Vishal M. Patel

[arXiv]

image

(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

Dataset preperation

  1. Download the relevant datasets: KITTI , Waymo , nuScenes

  2. 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.

| SECOND-iou |

Domain shift Model file Configuration file
Waymo -> KITTI download link
Waymo -> nuScenes download link
nuScenes -> KITTI download link

| PointRCNN |

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

Owner
Deepti Hegde
PhD student at Johns Hopkins University, Vision and Image Understanding Lab.
Deepti Hegde
GitHub Repository
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