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Found 8 papers, 1 papers with code
PseudoCal: Towards Initialisation-Free Deep Learning-Based Camera-LiDAR Self-Calibration
Camera-LiDAR extrinsic calibration is a critical task for multi-sensor fusion in autonomous systems, such as self-driving vehicles and mobile robots.
Analysis over vision-based models for pedestrian action anticipation
Therefore, we provide insights on the explainability of vision-based Transformer models in the context of pedestrian action prediction.
Learning to Estimate Two Dense Depths from LiDAR and Event Data
In this work, we propose to address these issues by fusing information from an event camera and a LiDAR using a learning-based approach to estimate accurate dense depth maps.
Emergent Visual Sensors for Autonomous Vehicles
Cameras are essential for perception systems due to the advantages of object detection and recognition provided by modern computer vision algorithms, comparing to other sensors, such as LiDARs and radars.
PreTR: Spatio-Temporal Non-Autoregressive Trajectory Prediction Transformer
Finally, we justify our usage of the parallel decoding technique by showing that the trajectory prediction task can be better solved as a non-autoregressive task.
Real-Time Optical Flow for Vehicular Perception with Low- and High-Resolution Event Cameras
As an answer to these points, we propose an optimized framework for computing optical flow in real-time with both low- and high-resolution event cameras.
Is attention to bounding boxes all you need for pedestrian action prediction?
Interestingly, we showed that pre-training our Transformer model on the CP2A dataset and then fine-tuning it on the PIE dataset is beneficial for the action prediction task.
Associative Embedding for Game-Agnostic Team Discrimination
Assigning team labels to players in a sport game is not a trivial task when no prior is known about the visual appearance of each team.
