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3d_euclidean_cluster

3d_line_detection

3d line detection from pointcloud using Hough Transform

aiimooc_lesson

睿慕课三维点云处理课程作业代码

autoware.universe

bert-keras

Keras implementation of BERT with pre-trained weights

bevdet

Official code base of the BEVDet series .

bevformer

This is the official implementation of BEVFormer, a camera-only framework for autonomous driving perception, e.g., 3D object detection and semantic map segmentation.

bevfusion

[ICRA'23] BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation

camera_instrinsic_calibration

camera intrinsic calibration from (almost) scratch for better understanding of intrinsic calibration mechanism

carnd-advanced-lane-lines

centerpillarnet

An anchor free method for pointcloud object detecion.

centerpoint

Export CenterPoint PonintPillars ONNX Model For TensorRT

centerpoint-1

convnext-v2

Code release for ConvNeXt V2 model

cuda-pointpillars

A project demonstrating how to use CUDA-PointPillars to deal with cloud points data from lidar.

curved-voxel-clustering-for-segmentation-of-3d-lidar-point-clouds

deep-learning-for-image-processing

deep learning for image processing including classification and object-detection etc.

fastai

The fastai deep learning library

fastertransformer

Transformer related optimization, including BERT, GPT

fastervit

Official PyTorch implementation of FasterViT: Fast Vision Transformers with Hierarchical Attention

fb-bev

Official PyTorch implementation of FB-BEV & FB-OCC - Forward-backward view transformation for vision-centric autonomous driving perception

ganet

A Keypoint-based Global Association Network for Lane Detection. Accepted by CVPR 2022

k-lane

The World's First Large Scale Lidar Lane Detection Dataset and Benchmark

k-radar

4D Radar Object Detection for Autonomous Driving in Various Weather Conditions

lane-detection-from-point-cloud

Detect lanes from the point cloud of a 80-meter highway road

lane-marking-detection

This is the final project for the Geospatial Vision and Visualization class at Northwestern University. The goal of the project is detecting the lane marking for a small LIDAR point cloud. Therefore, we cannot use a Deep Learning algorithm that learns to identify the lane markings by looking at a vast amount of data. Instead we will need to build a system that is able to identify the marking just by looking at the intensity value within the point cloud.

lane-marking-detection-tracking

large-scale_point_cloud_semantic_segmentation

latr

[ICCV2023 Oral] LATR: 3D Lane Detection from Monocular Images with Transformer

latte

LATTE: Accelerating LiDAR Point Cloud Annotation via Sensor Fusion, One-Click Annotation, and Tracking