Image2point: 3d point-cloud understanding with 2d image pretrained models
Abstract 3D point-clouds and 2D images are different visual representations of the physical
world. While human vision can understand both representations, computer vision models …
world. While human vision can understand both representations, computer vision models …
Unsupervised sampling promoting for stochastic human trajectory prediction
The indeterminate nature of human motion requires trajectory prediction systems to use a
probabilistic model to formulate the multi-modality phenomenon and infer a finite set of …
probabilistic model to formulate the multi-modality phenomenon and infer a finite set of …
Forecast-mae: Self-supervised pre-training for motion forecasting with masked autoencoders
This study explores the application of self-supervised learning (SSL) to the task of motion
forecasting, an area that has not yet been extensively investigated despite the widespread …
forecasting, an area that has not yet been extensively investigated despite the widespread …
Density-Adaptive Model Based on Motif Matrix for Multi-Agent Trajectory Prediction
Multi-agent trajectory prediction is essential in autonomous driving risk avoidance and traffic
flow control. However the heterogeneous traffic density on interactions which caused by …
flow control. However the heterogeneous traffic density on interactions which caused by …
T4P: Test-Time Training of Trajectory Prediction via Masked Autoencoder and Actor-specific Token Memory
Trajectory prediction is a challenging problem that requires considering interactions among
multiple actors and the surrounding environment. While data-driven approaches have been …
multiple actors and the surrounding environment. While data-driven approaches have been …
Exploiting map information for self-supervised learning in motion forecasting
Inspired by recent developments regarding the application of self-supervised learning (SSL),
we devise an auxiliary task for trajectory prediction that takes advantage of map-only …
we devise an auxiliary task for trajectory prediction that takes advantage of map-only …
Pre-training on synthetic driving data for trajectory prediction
Accumulating substantial volumes of real-world driving data proves pivotal in the realm of
trajectory forecasting for autonomous driving. Given the heavy reliance of current trajectory …
trajectory forecasting for autonomous driving. Given the heavy reliance of current trajectory …
Rmp: A random mask pretrain framework for motion prediction
As the pretraining technique is growing in popularity, little work has been done on pretrained
learning-based motion prediction methods in autonomous driving. In this paper, we propose …
learning-based motion prediction methods in autonomous driving. In this paper, we propose …
Sim-to-Real Causal Transfer: A Metric Learning Approach to Causally-Aware Interaction Representations
Modeling spatial-temporal interactions among neighboring agents is at the heart of multi-
agent problems such as motion forecasting and crowd navigation. Despite notable progress …
agent problems such as motion forecasting and crowd navigation. Despite notable progress …
Cohere3D: Exploiting Temporal Coherence for Unsupervised Representation Learning of Vision-based Autonomous Driving
Due to the lack of depth cues in images, multi-frame inputs are important for the success of
vision-based perception, prediction, and planning in autonomous driving. Observations from …
vision-based perception, prediction, and planning in autonomous driving. Observations from …