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xiaokn's Projects

a-loam icon a-loam

Advanced implementation of LOAM

apollo icon apollo

An open autonomous driving platform

apriltag icon apriltag

AprilTag is a visual fiducial system popular for robotics research.

avdata icon avdata

Autonomous Vehicle Seasonal Dataset

bevformer icon 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.

block-nerf icon block-nerf

This project is the non-official PyTorch implementation of Block-NeRF.

calibnet icon calibnet

Self-Supervised Extrinsic Calibration using 3D Spatial Transformer Networks

calibrcnn icon calibrcnn

CalibRCNN: Calibrating Camera and LiDAR by Recurrent Convolutional Neural Network and Geometric Constraints

cam_lidar_calibration icon cam_lidar_calibration

This package estimates the calibration parameters that transforms the camera frame (parent) into the lidar frame (child)

cartographer icon cartographer

Cartographer is a system that provides real-time simultaneous localization and mapping (SLAM) in 2D and 3D across multiple platforms and sensor configurations.

ccbv icon ccbv

Implementation of camera calibration following this CVPR 2020 paper : https://openaccess.thecvf.com/content_CVPR_2020/papers/Sha_End-to-End_Camera_Calibration_for_Broadcast_Videos_CVPR_2020_paper.pdf

chainer-regnet icon chainer-regnet

Chainer implementation of RegNet: Multimodal sensor registration using deep neural networks (with simplifications)

chatgpt-on-wechat icon chatgpt-on-wechat

Wechat robot based on ChatGPT, which using OpenAI api and itchat library. 使用大模型搭建微信聊天机器人,基于 GPT3.5/GPT4.0/Claude/文心一言/讯飞星火/LinkAI,支持个人微信、公众号、企业微信部署,能处理文本、语音和图片,访问操作系统和互联网,支持基于知识库定制专属机器人。

codes-for-pvkd icon codes-for-pvkd

Point-to-Voxel Knowledge Distillation for LiDAR Semantic Segmentation (CVPR 2022)

cvprojects icon cvprojects

computer vision projects | 计算机视觉相关好玩的AI项目(Python、C++)

dilu icon dilu

[ICLR 2024] DiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language Models

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