9 accepted papers
In this paper, we present DSERT-RoLL, a driving dataset that incorporates stereo event, RGB, and thermal cameras together with 4D radar and dual LiDAR, collected across diverse weather and illumination conditions. The dataset provides precise 2D and 3D bounding boxes with track IDs and ego vehicle o
Event cameras provide high-temporal-resolution, motion-centric measurements that remain reliable under fast motion and challenging illumination, making them a promising sensing modality for motion deblurring. However, existing deblurring methods typically require large-scale paired blur--sharp datas
With advancements in sensor and display technologies, high-resolution imagery is becoming increasingly prevalent in diverse applications. As a result, optical flow estimation needs to adapt to larger image resolutions, where even moderate movements lead to substantial pixel displacements, making lon…
Adverse weather conditions cause diverse and complex degradation patterns, driving the development of All-in-One (AiO) models. However, recent AiO solutions still struggle to capture diverse degradations, since global filtering methods like direct operations on the frequency domain fail to handle hi…
Multi-person pose estimation and tracking have been actively researched by the computer vision community due to their practical applicability. However, existing human pose estimation and tracking datasets have only been successful in typical scenarios, such as those without motion blur or with well-…
Video Frame Interpolation (VFI) which aims at generating high-frame-rate videos from low-frame-rate inputs is a highly challenging task. The emergence of bio-inspired sensors known as event cameras which boast microsecond-level temporal resolution has ushered in a transformative era for VFI. Nonethe…
"In low-light conditions, capturing videos with frame-based cameras often requires long exposure times, resulting in motion blur and reduced visibility. While frame-based motion deblurring and low-light enhancement have been studied, they still pose significant challenges. Event cameras have emerged…
"Event cameras with high dynamic range ensure scene capture even in low-light conditions. However, night events exhibit patterns different from those captured during the day. This difference causes performance degradation when applying night events to a model trained solely on day events. This limit…
Motion deblurring from a blurred image is a challenging computer vision problem because frame-based cameras lose information during the blurring process. Several attempts have compensated for the loss of motion information by using event cameras, which are bio-inspired sensors with a high temporal r…