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

Clean Machine Learning, a Coding Kata

Official code for NeurIPS 2021 paper "Towards Scalable Unpaired Virtual Try-On via Patch-Routed Spatially-Adaptive GAN"

Baseline model for "GraspNet-1Billion: A Large-Scale Benchmark for General Object Grasping" (CVPR 2020)

Build an Amazon SageMaker Pipeline to Transform Raw Texts to A Knowledge Graph

Contrastive Loss Gradient Attack (CLGA)

This GitHub repo consists of Code and Some results of project- Diabetes Treatment using Gold nanoparticles. These Consist of ML Models used for prediction Diabetes and further the basic theory and working of Gold nanoparticles.

Adversarial Adaptation with Distillation for BERT Unsupervised Domain Adaptation

Official repo of the paper "Surface Form Competition: Why the Highest Probability Answer Isn't Always Right"

Fully Connected DenseNet for Image Segmentation

ShinRL: A Library for Evaluating RL Algorithms from Theoretical and Practical Perspectives

Mapping Conditional Distributions for Domain Adaptation Under Generalized Target Shift

An Unpaired Sketch-to-Photo Translation Model

Repo for FUZE project. I will also publish some Linux kernel LPE exploits for various real world kernel vulnerabilities here. the samples are uploaded for education purposes for red and blue teams.

Official implementation of the paper "Light Field Networks: Neural Scene Representations with Single-Evaluation Rendering"

atmaCup #11 の Public 4th / Pricvate 5th Solution のリポジトリです。

CVPR 2022 "Online Convolutional Re-parameterization"

Code for the Population-Based Bandits Algorithm, presented at NeurIPS 2020.

Manage the availability of workspaces within Frappe/ ERPNext (sidebar) based on user-roles

Official implementation of NeurIPS'21: Implicit SVD for Graph Representation Learning

Offical implementation for "Trash or Treasure? An Interactive Dual-Stream Strategy for Single Image Reflection Separation".