Trajectory Prediction with Graph-based Dual-scale Context Fusion

Last update: Jan 04, 2023

Related tags

Deep Learning DSP

Overview

DSP: Trajectory Prediction with Graph-based Dual-scale Context Fusion

Introduction

This is the project page of the paper

Lu Zhang, Peiliang Li, Jing Chen and Shaojie Shen, "Trajectory Prediction with Graph-based Dual-scale Context Fusion", 2021,

which is submitted to the IEEE Robotics and Automation Letters (RA-L).

Notice: The code will be released after the publishing of this paper.

Preprint: Link

Video:

Demo

Color scheme: green - predicted trajectories; red - observation & GT trajectories; orange - other agents.

Owner

HKUST Aerial Robotics Group

GitHub Repository

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Trajectory Prediction with Graph-based Dual-scale Context Fusion

Related tags

Overview

DSP: Trajectory Prediction with Graph-based Dual-scale Context Fusion

Introduction

Demo

Owner

HKUST Aerial Robotics Group

Code of 3D Shape Variational Autoencoder Latent Disentanglement via Mini-Batch Feature Swapping for Bodies and Faces

Resources for our AAAI 2022 paper: "LOREN: Logic-Regularized Reasoning for Interpretable Fact Verification".

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Pytorch Implementation of rpautrat/SuperPoint

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