Real Time 3D Point Cloud Detection
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Updated
Apr 16, 2024 - Python
Real Time 3D Point Cloud Detection
Annotate 3D bounding boxes for 2D images with the link in description
This repository is for MORAI dataset training in 3D object detection with SECOND
Lidar Obstacle Detection using RANSAC and DBSCAN
Annotation File Converter is a GitHub repository that includes Python-based conversion scripts to convert annotations from one format to another.
The PyTorch Implementation based on YOLOX of the paper: "Complex-YOLO: Real-time 3D Object Detection on Point Clouds"
A comprehensive PyTorch framework for Semi-Supervised 3D Object Detection using LiDAR Point Clouds
Sejong creative semester system - 3D detection Paper Review
Graded projects of the course Deep Learning for Autonomous Driving, ETH Zürich (Spring 2021). Topics: Multi-task learning for semantics and depth, 3D Object Detection from Lidar Point Clouds.
A simple 3D Object Tracking webapp for humans built using streamlit.
[ECCV 2024] RecurrentBEV: A Long-term Temporal Fusion Framework for Multi-view 3D Detection
Some useful functions for working with the KITTI Dataset. Implementation of VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection.
Real-Time Hand Gesture-Driven 3D Object Manipulation
Implementation of SECOND in PyTorch for KITTI 3D Object Detetcion
A repository contained summaries and dissections of recent research papers in computer vision.
(Semi based Modified version) Virtual Sparse Convolution for Multimodal 3D Object Detection
the holistic detection detects face mesh ,hands and body postures.It can detect upto 30+fps.the project contains two different files one for 3d image detection and second for holistic detection. The project utilizes OpenCv, Python, MediaPipe API'S for detection.
A Robust, light-weight and unique 3D object detection architecture providing results (better than the conventional architectures) in real-time autonomous driving scenarios
Advanced Fast and Accurate 3D Object Detection using ResNet Architecture and Feature Pyramid Networks
PointVoxel-RCNN (PV-RCNN), is a two-stage 3D detection framework aiming at more accurate 3D object detection from point clouds. 3D detection approaches are based on either 3D voxel CNN with sparse convolution or PointNet-based networks as the backbone. 3D voxel CNNs with sparse convolution are more efficient and are able to generate high-quality…
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