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Found 8 papers, 2 papers with code
SSL-Interactions: Pretext Tasks for Interactive Trajectory Prediction
This paper addresses motion forecasting in multi-agent environments, pivotal for ensuring safety of autonomous vehicles.
SOAP: Cross-sensor Domain Adaptation for 3D Object Detection Using Stationary Object Aggregation Pseudo-labelling
We consider the problem of cross-sensor domain adaptation in the context of LiDAR-based 3D object detection and propose Stationary Object Aggregation Pseudo-labelling (SOAP) to generate high quality pseudo-labels for stationary objects.
Object Re-Identification from Point Clouds
To our knowledge, we are the first to study object re-identification from real point cloud observations.
Out-of-Distribution Detection for LiDAR-based 3D Object Detection
Detecting OOD inputs is challenging and essential for the safe deployment of models.
SSL-Lanes: Self-Supervised Learning for Motion Forecasting in Autonomous Driving
Self-supervised learning (SSL) is an emerging technique that has been successfully employed to train convolutional neural networks (CNNs) and graph neural networks (GNNs) for more transferable, generalizable, and robust representation learning.
Ranked #59 on
Motion Forecasting
on Argoverse CVPR 2020
LiDAR-MIMO: Efficient Uncertainty Estimation for LiDAR-based 3D Object Detection
The estimation of uncertainty in robotic vision, such as 3D object detection, is an essential component in developing safe autonomous systems aware of their own performance.
The missing link: Developing a safety case for perception components in automated driving
In this paper, we propose the Integration Safety Case for Perception (ISCaP), a generic template for such a linking safety argument specifically tailored for perception components.
SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection
In this paper, we propose two variants of self-attention for contextual modeling in 3D object detection by augmenting convolutional features with self-attention features.
Ranked #1 on
3D Object Detection
on KITTI Cyclists Hard
