There are 3 repositories under bev topic.
A 3D computer vision development toolkit based on PaddlePaddle. It supports point-cloud object detection, segmentation, and monocular 3D object detection models.
Fisheye or Normal Camera Intrinsic and Extrinsic Calibration. Surround Camera Bird Eye View Generator.
A basic implementation(not official code) of AVP-SLAM(IROS 2020) in simulation. https://arxiv.org/abs/2007.01813
BEVDet implemented by TensorRT, C++; Achieving real-time performance on Orin
国内首个占据栅格网络全栈课程《从BEV到Occupancy Network,算法原理与工程实践》,包含端侧部署。Surrounding Semantic Occupancy Perception Course for Autonomous Driving (docs, ppt and source code) 课程主页:http://111.229.117.200:7001/
DistillBEV: Boosting Multi-Camera 3D Object Detection with Cross-Modal Knowledge Distillation (ICCV 2023)
An efficient 3D semantic segmentation framework for Urban-scale point clouds like SensatUrban, Campus3D, etc.
Simple and Easy simulator YOLOv5 Object Detection with Bird's Eye View
[NeurIPS 2023] Asynchrony-Robust Collaborative Perception via Bird’s Eye View Flow
Generates 2D bird's eye view (BEV) images of individual LiDAR scans and aggregates individual scans to produce BEV maps.
QA script for Austrian address data in OSM, mirror of repo on Gitlab
Public charging station utilization dataset for the city of Hamburg. Dataset is described in the respective paper: Placing BEV Charging Infrastructure: Influencing Factors, Metrics, and their Influence on Observed Charger Utilization
YOLOv5 Object Detection with Automatic Braking System
Lane finding algorithm using image binarization and bird-eye perspective warping
Information about the software of and software updates for the Volkswagen ID. series electric vehicles (BEVs, e.g. VW ID.3, ID.4, etc.)
Tariff builder for ChargePointOperators in the area of employee, tradefairs, hotels or areal parking.
Implement a 3D object detection system with LIDAR/Fused data as input
Project: Generating overhead birds-eye-view occupancy grid map with semantic information from lidar and camera data.
BEV Representation of an Autonomous car using 6 RGB cameras by making use of Stable Diffusion Transformers
Changed the LBC algorithm, to support BEV and intreage it with ROS