Improving 3D Object Detection with Channel-wise Transformer

Last update: Dec 20, 2022

Related tags

Overview

"Improving 3D Object Detection with Channel-wise Transformer"

Thanks for the OpenPCDet, this implementation of the CT3D is mainly based on the pcdet v0.3. Our paper can be downloaded here ICCV2021.

Overview of CT3D. The raw points are first fed into the RPN for generating 3D proposals. Then the raw points along with the corresponding proposals are processed by the channel-wise Transformer composed of the proposal-to-point encoding module and the channel-wise decoding module. Specifically, the proposal-to-point encoding module is to modulate each point feature with global proposal-aware context information. After that, the encoded point features are transformed into an effective proposal feature representation by the channel-wise decoding module for confidence prediction and box regression.

	[email protected]	[email protected]	Download
Only Car	86.06	85.79	model-car
3-Category (Car)	85.04	84.97	model-3cat
3-Category (Pedestrian)	56.28	55.58	-
3-Category (Cyclist)	71.71	71.88	-

1. Recommended Environment

Linux (tested on Ubuntu 16.04)
Python 3.6+
PyTorch 1.1 or higher (tested on PyTorch 1.6)
CUDA 9.0 or higher (PyTorch 1.3+ needs CUDA 9.2+)

2. Set the Environment

pip install -r requirement.txt
python setup.py develop

3. Data Preparation

Prepare KITTI dataset and road planes

# Download KITTI and organize it into the following form:
├── data
│   ├── kitti
│   │   │── ImageSets
│   │   │── training
│   │   │   ├──calib & velodyne & label_2 & image_2 & (optional: planes)
│   │   │── testing
│   │   │   ├──calib & velodyne & image_2

# Generatedata infos:
python -m pcdet.datasets.kitti.kitti_dataset create_kitti_infos tools/cfgs/dataset_configs/kitti_dataset.yaml

Prepare Waymo dataset

# Download Waymo and organize it into the following form:
├── data
│   ├── 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

# Install tf 2.1.0
# Install the official waymo-open-dataset by running the following command:
pip3 install --upgrade pip
pip3 install waymo-open-dataset-tf-2-1-0 --user

# Extract point cloud data from tfrecord and generate data infos:
python -m pcdet.datasets.waymo.waymo_dataset --func create_waymo_infos --cfg_file tools/cfgs/dataset_configs/waymo_dataset.yaml

4. Train

Train with a single GPU

python train.py --cfg_file ${CONFIG_FILE}

# e.g.,
python train.py --cfg_file tools/cfgs/kitti_models/second_ct3d.yaml

Train with multiple GPUs or multiple machines

bash scripts/dist_train.sh ${NUM_GPUS} --cfg_file ${CONFIG_FILE}
# or 
bash scripts/slurm_train.sh ${PARTITION} ${JOB_NAME} ${NUM_GPUS} --cfg_file ${CONFIG_FILE}

# e.g.,
bash scripts/dist_train.sh 8 --cfg_file tools/cfgs/kitti_models/second_ct3d.yaml

5. Test

Test with a pretrained model:

python test.py --cfg_file ${CONFIG_FILE} --ckpt ${CKPT}

# e.g., 
python test.py --cfg_file tools/cfgs/kitti_models/second_ct3d.yaml --ckpt output/kitti_models/second_ct3d/default/kitti_val.pth

Improving 3D Object Detection with Channel-wise Transformer

Related tags

Overview

"Improving 3D Object Detection with Channel-wise Transformer"

1. Recommended Environment

2. Set the Environment

3. Data Preparation

4. Train

5. Test

Owner

Hualian Sheng

PiRank: Learning to Rank via Differentiable Sorting

Shuffle Attention for MobileNetV3

Personalized Federated Learning using Pytorch (pFedMe)

GNPy: Optical Route Planning and DWDM Network Optimization

Minimalistic PyTorch training loop

Custom studies about block sparse attention.

Weakly-supervised object detection.

The first dataset of composite images with rationality score indicating whether the object placement in a composite image is reasonable.

3DV 2021: Synergy between 3DMM and 3D Landmarks for Accurate 3D Facial Geometry

MERLOT: Multimodal Neural Script Knowledge Models

This is the official implementation of VaxNeRF (Voxel-Accelearated NeRF).

Pytorch implementation of forward and inverse Haar Wavelets 2D

Change is Everywhere: Single-Temporal Supervised Object Change Detection in Remote Sensing Imagery (ICCV 2021)

Optimizing Value-at-Risk and Conditional Value-at-Risk of Black Box Functions with Lacing Values (LV)

Python package provinding tools for artistic interactive applications using AI

Preprocessed Datasets for our Multimodal NER paper

View model summaries in PyTorch!

Official implementation of "Learning Forward Dynamics Model and Informed Trajectory Sampler for Safe Quadruped Navigation" (RSS 2022)

A copy of Ares that costs 30 fucking dollars.

PyTorch implementation of popular datasets and models in remote sensing