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Wei Sui's Projects

avp-slam-sim icon avp-slam-sim

A basic implementation(not official code) of AVP-SLAM(IROS 2020) in simulation. https://arxiv.org/abs/2007.01813

camodocal icon camodocal

CamOdoCal: Automatic Intrinsic and Extrinsic Calibration of a Rig with Multiple Generic Cameras and Odometry

clip icon clip

CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image

colmap icon colmap

COLMAP - Structure-from-Motion and Multi-View Stereo

ctlo icon ctlo

CTLO: Continuous-Time LiDAR Odometry

ctto icon ctto

Continuous time dTof odometry

cube_slam icon cube_slam

CubeSLAM: Monocular 3D Object Detection and SLAM

curve-gcn icon curve-gcn

Official PyTorch code for Curve-GCN (CVPR 2019)

deep-video-mvs icon deep-video-mvs

Code for "DeepVideoMVS: Multi-View Stereo on Video with Recurrent Spatio-Temporal Fusion" (CVPR 2021)

deep3dfacereconstruction icon deep3dfacereconstruction

Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set (CVPRW 2019)

dm-vio icon dm-vio

Coming soon: Source code forthe paper DM-VIO: Delayed Marginalization Visual-Inertial Odometry

dynaslam icon dynaslam

DynaSLAM is a SLAM system robust in dynamic environments for monocular, stereo and RGB-D setups

ekf_imu_gps icon ekf_imu_gps

Extended Kalman Filter predicts the GNSS measurement based on IMU measurement

f2-nerf icon f2-nerf

Fast neural radiance field training with free camera trajectories

ganet icon ganet

GA-Net: Guided Aggregation Net for End-to-end Stereo Matching

gtsam icon gtsam

GTSAM is a library of C++ classes that implement smoothing and mapping (SAM) in robotics and vision, using factor graphs and Bayes networks as the underlying computing paradigm rather than sparse matrices.

idol icon idol

Official implementation of "IDOL: Inertial Deep Orientation-estimation & Localization." AAAI 2021.

igev icon igev

[CVPR 2023] Iterative Geometry Encoding Volume for Stereo Matching and Multi-View Stereo

lego-loam icon lego-loam

LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain

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