[ICLR'23 Spotlight & IJCV'24] MapTR: Structured Modeling and Learning for Online Vectorized HD Map Construction
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Updated
May 13, 2024 - Python
[ICLR'23 Spotlight & IJCV'24] MapTR: Structured Modeling and Learning for Online Vectorized HD Map Construction
Fisheye or Normal Camera Intrinsic and Extrinsic Calibration. Surround Camera Bird Eye View Generator.
A 3D computer vision development toolkit based on PaddlePaddle. It supports point-cloud object detection, segmentation, and monocular 3D object detection models.
A basic implementation(not official code) of AVP-SLAM(IROS 2020) in simulation. https://arxiv.org/abs/2007.01813
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) 在线课程主页:http://111.229.117.200:8100/ (作者独立搭建)
[ICCV2023] Official Implementation of "UniTR: A Unified and Efficient Multi-Modal Transformer for Bird’s-Eye-View Representation"
BEVDet implemented by TensorRT, C++; Achieving real-time performance on Orin
[ECCV 2024] This is the official implementation of MapQR, an end-to-end method with an emphasis on enhancing query capabilities for constructing online vectorized maps.
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.
[NeurIPS 2023] Asynchrony-Robust Collaborative Perception via Bird’s Eye View Flow
[NeurIPS 2023] Fine-Grained Cross-View Geo-Localization Using a Correlation-Aware Homography Estimator
Pytorch implementation of the paper 'CoBEV: Elevating Roadside 3D Object Detection with Depth and Height Complementarity'
[IROS 2024 Oral] BEV$^2$PR: BEV-Enhanced Visual Place Recognition with Structural Cues
Tool to design and optimize autonomous vehicle concepts.
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
QA script for Austrian address data in OSM, mirror of repo on Gitlab
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