Experimental dockerized workspace for 2D/3D object detection, segmentation, tracking, anything related to perception
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Updated
Sep 30, 2020 - Jupyter Notebook
Experimental dockerized workspace for 2D/3D object detection, segmentation, tracking, anything related to perception
A Simple PointPillars PyTorch Implementation for 3D Lidar(KITTI) Detection.
Deployment of 3D-Detection and Tracking pipeline in simulation based on rosbags and real-time.
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
Implementation of PointPillars paper in Unity for VRChat
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
Implementation of PointPillars Network with LiDAR-camera fusion for 3D Object Detection in Autonomous Driving.
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
Convert pointpillars Pytorch Model To ONNX for TensorRT Inference
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.
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