As autonomous driving technology matures, end-to-end methodologies have emerged as a
leading strategy, promising seamless integration from perception to control via deep …

Abstract Segment Anything Model (SAM) has achieved impressive performance in many
computer vision tasks. However as a large-scale model the immense memory and …

Mapping is crucial for spatial reasoning, planning and robot navigation. Existing approaches
range from metric, which require precise geometry-based optimization, to purely topological …

Simulators are powerful tools for autonomous robot learning as they offer scalable data
generation, flexible design, and optimization of trajectories. However, transferring behavior …

Challenging motion, resulting in serious motion artifacts, is a well-known problem in
structured-light (SL) 3-D scanning. Single-shot imaging or tracking interframe offsets …

We provide a sober look at the application of Multimodal Large Language Models (MLLMs)
within the domain of autonomous driving and challenge/verify some common assumptions …

Salient Object Detection (SOD) aims to identify and segment the most prominent objects in
images. Advanced SOD methods often utilize various Convolutional Neural Networks (CNN) …

Easily accessible sensors, like drones with diverse onboard sensors, have greatly expanded
studying animal behavior in natural environments. Yet, analyzing vast, unlabeled video data …

The field of computer vision is currently transitioning from closed-set to open-set tasks.
Vision foundation models have already demonstrated success in open-set scenarios …

Object tracking is a fundamental task in computer vision with broad practical applications
across various domains, including traffic monitoring, robotics, and autonomous vehicle …