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pynancy's Projects

3dfeatnet icon 3dfeatnet

3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registration

cae-lo icon cae-lo

CAE-LO: LiDAR Odometry Leveraging Fully Unsupervised Convolutional Auto-Encoder for Interest Point Detection and Feature Description

codes-for-pvkd icon codes-for-pvkd

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

cvml_project icon cvml_project

Projects and application using computer vision and machine learning

deeplearning-500-questions icon deeplearning-500-questions

深度学习500问,以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述,以帮助自己及有需要的读者。 全书分为18个章节,50余万字。由于水平有限,书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作,联系[email protected] 版权所有,违权必究 Tan 2018.06

fast_lio icon fast_lio

A computationally efficient and robust LiDAR-inertial odometry (LIO) package

instant-ngp icon instant-ngp

Instant neural graphics primitives: lightning fast NeRF and more

lt-mapper icon lt-mapper

A Modular Framework for LiDAR-based Lifelong Mapping

mars icon mars

MARS: An Instance-aware, Modular and Realistic Simulator for Autonomous Driving

ml-from-scratch icon ml-from-scratch

Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.

models icon models

Models and examples built with TensorFlow

mulls icon mulls

MULLS: Versatile LiDAR SLAM via Multi-metric Linear Least Square [ICRA '21]

open3d-ml icon open3d-ml

An extension of Open3D to address 3D Machine Learning tasks

open3d_slam icon open3d_slam

Pointcloud-based graph SLAM written in C++ using open3D library.

openpsg icon openpsg

Benchmarking Panoptic Scene Graph Generation (PSG), ECCV'22

pyslam icon pyslam

pySLAM contains a monocular Visual Odometry (VO) pipeline in Python. It supports many modern local features based on Deep Learning.

python icon python

All Algorithms implemented in Python

pytorch-gan icon pytorch-gan

PyTorch implementations of Generative Adversarial Networks.

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