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Tau_li123's Projects

rma-net icon rma-net

the code of the paper: Recurrent Multi-view Alignment Network for Unsupervised Surface Registration (CVPR 2021)

robo3d icon robo3d

Robo3D: Towards Robust and Reliable 3D Perception against Corruptions

ros2_jetson_stats icon ros2_jetson_stats

ROS 2 package for monitoring and controlling NVIDIA Jetson Platform resources

rpmnet icon rpmnet

RPM-Net: Robust Point Matching using Learned Features (CVPR2020)

rubick icon rubick

🔧 Electron based open source toolbox, free integration of rich plug-ins. 基于 electron 的开源工具箱,自由集成丰富插件。

sa-ssd icon sa-ssd

SA-SSD: Structure Aware Single-stage 3D Object Detection from Point Cloud (CVPR 2020)

segment-anything icon segment-anything

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

segment-geospatial icon segment-geospatial

A Python package for segmenting geospatial data with the Segment Anything Model (SAM)

sensaturban icon sensaturban

🔥Urban-scale point cloud dataset (CVPR 2021 & IJCV 2022)

singleshotpose icon singleshotpose

This research project implements a real-time object detection and pose estimation method as described in the paper, Tekin et al. "Real-Time Seamless Single Shot 6D Object Pose Prediction", CVPR 2018. (https://arxiv.org/abs/1711.08848).

slamesh icon slamesh

ICRA2023, A real-time LiDAR simultaneous localization and meshing method.

softgroup icon softgroup

[CVPR 2022 Oral] SoftGroup for Instance Segmentation on 3D Point Clouds

sophus icon sophus

C++ implementation of Lie Groups using Eigen.

spinnet icon spinnet

[CVPR 2021] SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration

superpoint_transformer icon superpoint_transformer

[ICCV'23] Official PyTorch implementation of Superpoint Transformer introduced in "Efficient 3D Semantic Segmentation with Superpoint Transformer"

synthcp icon synthcp

Offical code base for the ECCV oral paper "Synthesize then Compare: Detecting Failures and Anomalies for Semantic Segmentation"

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