pointpillars

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

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

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.

Implementation of PointPillars in PyTorch for KITTI 3D Object Detetcion

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.

C++/CUDA PointPillars LiDAR preprocessing pipeline with KITTI loading, pillar scatter, BEV pseudo-image generation, tests, and CUDA range filtering.

working repository

Reproducible 3D LiDAR detection with TensorRT FP16, exact deterministic voxelization, and ROS 2.

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.

ROS2 PointPillars inference demo using OpenPCDet and pybind11 for LiDAR point cloud object detection.

Initialisation-free camera–LiDAR self-calibration via pseudo-LiDAR BEV pillars (BMVC 2023). Recovers extreme decalibration — up to ±180° yaw — in one shot, then refines through a 3-stage coarse-to-fine cascade.

3D BEV object detection · mAP 0.624 on nuScenes · LiDAR-camera fusion · PyTorch

This repository serves as a client to send sensor messages from ROS or other sources to the Inference server and processes the inference results.

PointPillars for Argoverse object detection