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Name: Honghui Wang
Type: User
Company: UNSW
Bio: Mphil candidate at School of Mathematics and Statistics of UNSW
Location: Sydney
Name: Honghui Wang
Type: User
Company: UNSW
Bio: Mphil candidate at School of Mathematics and Statistics of UNSW
Location: Sydney
A beautiful, simple, clean, and responsive Jekyll theme for academics
:sunglasses: A curated list of robotics libraries and software
This is a list of papers related to traffic agent trajectory prediction.
The repository is used to visualize trajectories in the domain of pedestrian trajectory prediction
Stable dynamical system learning using Euclideanizing flows
ICCV2021 Expert-Goal Trajectory Prediction
Framework for Easily Invertible Architectures
This is my trial to use goal predictor in the Social-LSTM algorithm for complete pedestrian trajectory prediction
Goal-conditioned deep learning for trajectory prediction is to predict the goal position in advance and then use the estimated goal positions to forecast the trajectory. For research use only.
Kernel Trajectory Maps CoRL 2019
[CVPR2023] Leapfrog Diffusion Model for Stochastic Trajectory Prediction
[CVPR2022] Code for CVPR 2022 paper "Stochastic Trajectory Prediction via Motion Indeterminacy Diffusion"
This is my mphil thesis of UNSW. It involves all codes presented in the thesis including well-known algorithms and my proposed algorithm
Official Code for "Non-Probability Sampling Network for Stochastic Human Trajectory Prediction (CVPR 2022)"
This is the code for occupancy grid map used in pedestrian trajectory prediction from CVPR2022 <End-to-End Trajectory Prediction Based on Occupancy Grid Maps>
Human Trajectory Prediction Dataset Benchmark (ACCV 2020)
This is the group project involving front end, back end, and algorithm.
The social-LSTM code for complete trajectory prediction (20 frames). In this repository, the normalized trajectory and non-normalized trajectory are used respectively.
[ECCV2022] SocialVAE: Human Trajectory Prediction using Timewise Latents
[ECCV 2020] Code for "Spatio-Temporal Graph Transformer Networks for Pedestrian Trajectory Prediction"
Code accompanying the ECCV 2020 paper "Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data" by Tim Salzmann*, Boris Ivanovic*, Punarjay Chakravarty, and Marco Pavone (* denotes equal contribution).
Caffe code to accompany my Tutorial on Variational Autoencoders
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Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.