A 3D computer vision development toolkit based on PaddlePaddle. It supports point-cloud object detection, segmentation, and monocular 3D object detection models.

A Simple PointPillars PyTorch Implementation for 3D LiDAR(KITTI) Detection.

Convert pointpillars Pytorch Model To ONNX for TensorRT Inference

PAPC is a deep learning for point clouds platform based on pure PaddlePaddle

Frustum-PointPillars: A Multi-Stage Approach for 3D Object Detection using RGB Camera and LiDAR

Implementation of PointPillars Network with LiDAR-camera fusion for 3D Object Detection in Autonomous Driving.

Implementation of PointPillars paper in Unity for VRChat

Deployment of 3D-Detection and Tracking pipeline in simulation based on rosbags and real-time.

This repository contains a Master's Thesis on enhancing LiDAR-based 3D Object Detection in autonomous vehicles using synthetic data. The research explores the use of the Ansys AVxcelerate Sensors Simulator (AVX) to create synthetic point clouds. It includes a hybrid training approach, experimental design, results, and related scripts.

Implementation of PointPillars in PyTorch for KITTI 3D Object Detetcion

working repository

Experimental dockerized workspace for 2D/3D object detection, segmentation, tracking, anything related to perception

This repository contains supplementary materials for a Master Thesis project on efficient object detection. It includes a series of video demonstrations showcasing the performance of networks trained and tested on synthetic (AVX) and real-world (KITTI) data sets.

Anchor-Free LiDAR object detetor for 3d Object detection

PointPillars for Argoverse object detection

A Simple PointPillars PyTorch Implementation for 3D Lidar(KITTI) Detection.