【ICCV 2021】DepthContrast：Self-Supervised Pretraining of 3D Features on any Point-Cloud - 代码天地

【ICCV 2021】DepthContrast：Self-Supervised Pretraining of 3D Features on any Point-Cloud

企业开发 2022-05-12 17:32:57 阅读次数: 0

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转载自blog.csdn.net/weixin_43154149/article/details/124584331

【ICCV 2021】DepthContrast：Self-Supervised Pretraining of 3D Features on any Point-Cloud

【论文翻译】【ICCV_2021_精】DepthContrast：Self-Supervised Pretraining of 3D Features on any Point-Cloud

点云 3D 天气数据增强 - Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in ... (ICCV 2021)

点云 3D 目标跟踪 - SimTrack（ICCV 2021）

点云 3D 目标检测 - RangeDet（ICCV 2021）

【WACV 2021】Self-Supervised Learning for Domain Adaptation on Point Clouds

【ICCV 2023】3DPPE: 3D Point Positional Encoding for Multi-Camera 3D Object Detection Transformers

【ICCV 2019】MAP-VAE：Multi-Angle Point Cloud-VAE： Unsupervised Feature Learning for 3D Point Clouds..

PPFNet: Global Context Aware Local Features for Robust 3D Point Matching 阅读

论文阅读：EPNet: Enhancing Point Features with ImageSemantics for 3D Object Detection

3D box经典论文-《Multimodal 3D Object Detection fromSimulated Pretraining》学习记录

【论文阅读】【ICCV 2021】CurveNet：Walk in the Cloud： Learning Curves for Point Clouds Shape Analysis

【ICCV 2021】A Closer Look at Rotation-invariant Deep Point Cloud Analysis

3D Packing for Self-Supervised Monocular Depth Estimation论文笔记

读Self-supervised Single-view 3D Reconstruction via Semantic Consistency

3D Point Cloud Library install

【论文阅读】【2201.00785v2】Implicit Autoencoder for Point Cloud Self-supervised Representation Learning

论文解读：SuperPoint: Self-Supervised Interest Point Detection and Description

【论文阅读】Learning Spatiotemporal Features with 3D Convolutional Networks

CVPR 2019 Self-Supervised Learning of 3D Human Pose using Multi-view Geometry 论文阅读

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#ICCV2019论文阅读#Fully_convolutional_Features

【2021】PnP-3D: A Plug-and-Play for 3D Point Clouds

Highlight point features

【3D deep learning on Point cloud】(1)

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【ICCV 2023】FocalFormer3D : Focusing on Hard Instance for 3D Object Detection

C3D - Learning Spatiotemporal Features with 3D Convolutional Networks

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