3D cascade RCNN for object detection on point cloud

Related tags

Deep Learningpoint-cloudobject-detection3d-object-detectioncascade-detection
Overview

3D Cascade RCNN

This is the implementation of 3D Cascade RCNN: High Quality Object Detection in Point Clouds.

We designed a 3D object detection model on point clouds by:

  • Presenting a simple yet effective 3D cascade architecture
  • Analyzing the sparsity of the point clouds and using point completeness score to re-weighting training samples. Following is detection results on Waymo Open Dataset.

Results on KITTI

Easy Car Moderate Car Hard Car
AP 11 90.05 86.02 79.27
AP 40 93.20 86.19 83.48

Results on Waymo

Overall Vehicle 0-30m Vehicle 30-50m Vehicle 50m-Inf Vehicle
LEVEL_1 mAP 76.27 92.66 74.99 54.49
LEVEL_2 mAP 67.12 91.95 68.96 41.82

Installation

  1. Requirements. The code is tested on the following environment:
  • Ubuntu 16.04 with 4 V100 GPUs
  • Python 3.7
  • Pytorch 1.7
  • CUDA 10.1
  • spconv 1.2.1
  1. Build extensions
python setup.py develop

Getting Started

Prepare for the data.

Please download the official KITTI dataset and generate data infos by following command:

python -m pcdet.datasets.kitti.kitti_dataset create_kitti_infos tools/cfgs/kitti_dataset.yaml

The folder should be like:

data
├── kitti
│   │── ImageSets
│   │── training
│   │   ├──calib & velodyne & label_2 & image_2
│   │── testing
│   │   ├──calib & velodyne & image_2
|   |── kitti_dbinfos_train.pkl
|   |── kitti_infos_train.pkl
|   |── kitti_infos_val.pkl

Training and evaluation.

The configuration file is in tools/cfgs/3d_cascade_rcnn.yaml, and the training scripts is in tools/scripts.

cd tools
sh scripts/3d-cascade-rcnn.sh

Test a pre-trained model

The pre-trained KITTI model is at: model. Run with:

cd tools
sh scripts/3d-cascade-rcnn_test.sh

The evaluation results should be like:

2021-08-10 14:06:14,608   INFO  Car [email protected], 0.70, 0.70:
bbox AP:97.9644, 90.1199, 89.7076
bev  AP:90.6405, 89.0829, 88.4391
3d   AP:90.0468, 86.0168, 79.2661
aos  AP:97.91, 90.00, 89.48
Car [email protected], 0.70, 0.70:
bbox AP:99.1663, 95.8055, 93.3149
bev  AP:96.3107, 92.4128, 89.9473
3d   AP:93.1961, 86.1857, 83.4783
aos  AP:99.13, 95.65, 93.03
Car [email protected], 0.50, 0.50:
bbox AP:97.9644, 90.1199, 89.7076
bev  AP:98.0539, 97.1877, 89.7716
3d   AP:97.9921, 90.1001, 89.7393
aos  AP:97.91, 90.00, 89.48
Car [email protected], 0.50, 0.50:
bbox AP:99.1663, 95.8055, 93.3149
bev  AP:99.1943, 97.8180, 95.5420
3d   AP:99.1717, 95.8046, 95.4500
aos  AP:99.13, 95.65, 93.03

Acknowledge

The code is built on OpenPCDet and Voxel R-CNN.

Owner
Qi Cai
Qi Cai
GitHub Repository
A large-image collection explorer and fast classification tool

IMAX: Interactive Multi-image Analysis eXplorer This is an interactive tool for visualize and classify multiple images at a time. It written in Python

Matias Carrasco Kind 23 Dec 16, 2022
Randomized Correspondence Algorithm for Structural Image Editing

===================================== README: Inpainting based PatchMatch ===================================== @Author: Younesse ANDAM @Conta

Younesse 116 Dec 24, 2022
The GitHub repository for the paper: “Time Series is a Special Sequence: Forecasting with Sample Convolution and Interaction“.

SCINet This is the original PyTorch implementation of the following work: Time Series is a Special Sequence: Forecasting with Sample Convolution and I

386 Jan 01, 2023
[ICCV'21] Learning Conditional Knowledge Distillation for Degraded-Reference Image Quality Assessment

CKDN The official implementation of the ICCV2021 paper "Learning Conditional Knowledge Distillation for Degraded-Reference Image Quality Assessment" O

Multimedia Research 50 Dec 13, 2022
A Demo server serving Bert through ONNX with GPU written in Rust with <3

Demo BERT ONNX server written in rust This demo showcase the use of onnxruntime-rs on BERT with a GPU on CUDA 11 served by actix-web and tokenized wit

Xavier Tao 28 Jan 01, 2023
Face Mesh is a face geometry solution that estimates 468 3D face landmarks in real-time even on mobile devices

Face-Mesh Face Mesh is a face geometry solution that estimates 468 3D face landmarks in real-time even on mobile devices. It employs machine learning

Farnam Javadi 9 Dec 21, 2022
CARMS: Categorical-Antithetic-REINFORCE Multi-Sample Gradient Estimator

CARMS: Categorical-Antithetic-REINFORCE Multi-Sample Gradient Estimator This is the official code repository for NeurIPS 2021 paper: CARMS: Categorica

Alek Dimitriev 1 Jul 09, 2022
Recurrent Scale Approximation (RSA) for Object Detection

Recurrent Scale Approximation (RSA) for Object Detection Codebase for Recurrent Scale Approximation for Object Detection in CNN published at ICCV 2017

Yu Liu (Louis) 239 Dec 28, 2022
Python Implementation of algorithms in Graph Mining, e.g., Recommendation, Collaborative Filtering, Community Detection, Spectral Clustering, Modularity Maximization, co-authorship networks.

Graph Mining Author: Jiayi Chen Time: April 2021 Implemented Algorithms: Network: Scrabing Data, Network Construbtion and Network Measurement (e.g., P

Jiayi Chen 3 Mar 03, 2022
Anchor Retouching via Model Interaction for Robust Object Detection in Aerial Images

Anchor Retouching via Model Interaction for Robust Object Detection in Aerial Images In this paper, we present an effective Dynamic Enhancement Anchor

13 Dec 09, 2022
A library for differentiable nonlinear optimization.

Theseus A library for differentiable nonlinear optimization built on PyTorch to support constructing various problems in robotics and vision as end-to

Meta Research 1.1k Dec 30, 2022
A package related to building quasi-fibration symmetries

qf A package related to building quasi-fibration symmetries. If you'd like to learn more about how it works, see the brief explanation and References

Paolo Boldi 1 Dec 01, 2021
A PyTorch Implementation of Gated Graph Sequence Neural Networks (GGNN)

A PyTorch Implementation of GGNN This is a PyTorch implementation of the Gated Graph Sequence Neural Networks (GGNN) as described in the paper Gated G

Ching-Yao Chuang 427 Dec 13, 2022
Learning Time-Critical Responses for Interactive Character Control

Learning Time-Critical Responses for Interactive Character Control Abstract This code implements the paper Learning Time-Critical Responses for Intera

Movement Research Lab 227 Dec 31, 2022
Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification (NeurIPS 2021)

Graph Posterior Network This is the official code repository to the paper Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classifica

Maximilian Stadler 30 Dec 05, 2022
Funnels: Exact maximum likelihood with dimensionality reduction.

Funnels This repository contains the code needed to reproduce the experiments from the paper: Funnels: Exact maximum likelihood with dimensionality re

2 Apr 21, 2022
This code reproduces the results of the paper, "Measuring Data Leakage in Machine-Learning Models with Fisher Information"

Fisher Information Loss This repository contains code that can be used to reproduce the experimental results presented in the paper: Awni Hannun, Chua

Facebook Research 43 Dec 30, 2022
[CVPR 2021] Few-shot 3D Point Cloud Semantic Segmentation

Few-shot 3D Point Cloud Semantic Segmentation Created by Na Zhao from National University of Singapore Introduction This repository contains the PyTor

117 Dec 27, 2022
This repository contains the official code of the paper Equivariant Subgraph Aggregation Networks (ICLR 2022)

Equivariant Subgraph Aggregation Networks (ESAN) This repository contains the official code of the paper Equivariant Subgraph Aggregation Networks (IC

Beatrice Bevilacqua 59 Dec 13, 2022
EZ graph is an easy to use AI solution that allows you to make and train your neural networks without a single line of code.

EZ-Graph EZ Graph is a GUI that allows users to make and train neural networks without writing a single line of code. Requirements python 3 pandas num

1 Jul 03, 2022