PyTorch Implementation of PointPillars
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Updated
Feb 24, 2022 - Python
PyTorch Implementation of PointPillars
Advanced Fast and Accurate 3D Object Detection using ResNet Architecture and Feature Pyramid Networks
Real Time 3D Point Cloud Detection
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…
A two stage multi-modal loss model along with rigid body transformations to regress 3D bounding boxes
Some useful functions for working with the KITTI Dataset. Implementation of VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection.
Lidar Obstacle Detection using RANSAC and DBSCAN
3D Object Detection by Colorful Pointcloud. Alignment Between RGB image and Lidar Pointcloud
Real-Time Hand Gesture-Driven 3D Object Manipulation
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.
[ECCV2024] This is the official implementation of GraphBEV, a BEV multi-modal framework for autonomous driving perception, e.g., 3D object detection and semantic map segmentation.
[ECCV 2024] RecurrentBEV: A Long-term Temporal Fusion Framework for Multi-view 3D Detection
Frustum PointNets for 3D Object Detection from RGB-D Data
Implementation of SECOND in PyTorch for KITTI 3D Object Detetcion
Annotation File Converter is a GitHub repository that includes Python-based conversion scripts to convert annotations from one format to another.
Official codebase of HyDRa.
Master Thesis: Weakly Supervised Monocular 3D Object Detection
Advanced 3D Multi Object Tracking using Multi Model Kalman Filter using Long Range and Short Range Tendency for Autonomous Vehicle
Notes and key takeaways of the Self-Driving Cars Perception applied Deep Learning Free Course from freeCodeCamp.org
[ECCV 2024] Ray Denoising (RayDN): Depth-aware Hard Negative Sampling for Multi-view 3D Object Detection
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