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
Classical OCR DCNN reproduction based on PaddlePaddle framework.

Paddle-SVHN Classical OCR DCNN reproduction based on PaddlePaddle framework. This project reproduces Multi-digit Number Recognition from Street View I

1 Nov 12, 2021
This repository provides the official code for GeNER (an automated dataset Generation framework for NER).

GeNER This repository provides the official code for GeNER (an automated dataset Generation framework for NER). Overview of GeNER GeNER allows you to

DMIS Laboratory - Korea University 50 Nov 30, 2022
Building blocks for uncertainty-aware cycle consistency presented at NeurIPS'21.

UncertaintyAwareCycleConsistency This repository provides the building blocks and the API for the work presented in the NeurIPS'21 paper Robustness vi

EML Tübingen 19 Dec 12, 2022
Yoga - Yoga asana classifier for python

Yoga Asana Classifier Description Hi welcome to my new deep learning project "Yo

Programminghut 35 Dec 12, 2022
Adjust Decision Boundary for Class Imbalanced Learning

Adjusting Decision Boundary for Class Imbalanced Learning This repository is the official PyTorch implementation of WVN-RS, introduced in Adjusting De

Peyton Byungju Kim 16 Jan 04, 2023
Mememoji - A facial expression classification system that recognizes 6 basic emotions: happy, sad, surprise, fear, anger and neutral.

a project built with deep convolutional neural network and ❤️ Table of Contents Motivation The Database The Model 3.1 Input Layer 3.2 Convolutional La

Jostine Ho 761 Dec 05, 2022
Sum-Product Probabilistic Language

Sum-Product Probabilistic Language SPPL is a probabilistic programming language that delivers exact solutions to a broad range of probabilistic infere

MIT Probabilistic Computing Project 57 Nov 17, 2022
Neural Contours: Learning to Draw Lines from 3D Shapes (CVPR2020)

Neural Contours: Learning to Draw Lines from 3D Shapes This repository contains the PyTorch implementation for CVPR 2020 Paper "Neural Contours: Learn

93 Dec 16, 2022
Machine Learning University: Accelerated Computer Vision Class

Machine Learning University: Accelerated Computer Vision Class This repository contains slides, notebooks, and datasets for the Machine Learning Unive

AWS Samples 1.3k Dec 28, 2022
Autoencoders pretraining using clustering

Autoencoders pretraining using clustering

IITiS PAN 2 Dec 16, 2021
Yolov5-lite - Minimal PyTorch implementation of YOLOv5

Yolov5-Lite: Minimal YOLOv5 + Deep Sort Overview This repo is a shortened versio

Kadir Nar 57 Nov 28, 2022
MediaPipe Kullanarak İleri Seviye Bilgisayarla Görü

MediaPipe Kullanarak İleri Seviye Bilgisayarla Görü

Burak Bagatarhan 12 Mar 29, 2022
Localizing Visual Sounds the Hard Way

Localizing-Visual-Sounds-the-Hard-Way Code and Dataset for "Localizing Visual Sounds the Hard Way". The repo contains code and our pre-trained model.

Honglie Chen 58 Dec 07, 2022
U-Net Implementation: Convolutional Networks for Biomedical Image Segmentation" using the Carvana Image Masking Dataset in PyTorch

U-Net Implementation By Christopher Ley This is my interpretation and implementation of the famous paper "U-Net: Convolutional Networks for Biomedical

Christopher Ley 1 Jan 06, 2022
Official code for "Mean Shift for Self-Supervised Learning"

MSF Official code for "Mean Shift for Self-Supervised Learning" Requirements Python = 3.7.6 PyTorch = 1.4 torchvision = 0.5.0 faiss-gpu = 1.6.1 In

UMBC Vision 44 Nov 21, 2022
Benchmarking Pipeline for Prediction of Protein-Protein Interactions

B4PPI Benchmarking Pipeline for the Prediction of Protein-Protein Interactions How this benchmarking pipeline has been built, and how to use it, is de

Loïc Lannelongue 4 Jun 27, 2022
Multi-layer convolutional LSTM with Pytorch

Convolution_LSTM_pytorch Thanks for your attention. I haven't got time to maintain this repo for a long time. I recommend this repo which provides an

Zijie Zhuang 734 Jan 03, 2023
This repository is for Contrastive Embedding Distribution Refinement and Entropy-Aware Attention Network (CEDR)

CEDR This repository is for Contrastive Embedding Distribution Refinement and Entropy-Aware Attention Network (CEDR) introduced in the following paper

phoenix 3 Feb 27, 2022
YOLOV4运行在嵌入式设备上

在嵌入式设备上实现YOLO V4 tiny 在嵌入式设备上实现YOLO V4 tiny 目录结构 目录结构 |-- YOLO V4 tiny |-- .gitignore |-- LICENSE |-- README.md |-- test.txt |-- t

Liu-Wei 6 Sep 09, 2021
The official repo of the CVPR 2021 paper Group Collaborative Learning for Co-Salient Object Detection .

GCoNet The official repo of the CVPR 2021 paper Group Collaborative Learning for Co-Salient Object Detection . Trained model Download final_gconet.pth

Qi Fan 46 Nov 17, 2022