We focus on a very challenging task: imaging at nighttime dynamic scenes. Conventional RGB cameras struggle with the trade-off between long exposure for low-light imaging and short exposure for capturing dynamic scenes. Event cameras react to dynamic changes, with their high temporal resolution (microsecond) and dynamic range (120 dB), and thus offer a promising alternative. However, existing methods are mostly based on simulated datasets due to the lack of paired event-clean image data for nighttime conditions, where the domain gap leads to performance limitations in real-world scenarios. Moreover, most existing event reconstruction methods are tailored for daytime data, overlooking issues unique to low-light events at night, such as strong noise, temporal trailing, and spatial non-uniformity, resulting in unsatisfactory reconstruction results. To address these challenges, we construct the first real paired low-light event dataset (RLED) through a co-axial imaging system, comprising 80,400 spatially and temporally aligned image GTs and low-light events, which provides a unified training and evaluation dataset for existing methods. We further conduct a comprehensive analysis of the causes and characteristics of strong noise, temporal trailing, and spatial non-uniformity in nighttime events, and propose a nighttime event reconstruction network (NER-Net+). It includes a learnable event timestamps calibration module (LETC) to correct the temporal trailing events and a non-stationary spatio-temporal information enhancement module (NSIE) to suppress sensor noise and spatial non-uniformity. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art methods in visual quality and generalization on real-world nighttime datasets.

We focus on a very challenging task: imaging at night-time dynamic scenes. Most previous methods rely on the low-light enhancement of a conventional RGB camera. However, they would inevitably face a dilemma between the long exposure time of nighttime and the motion blur of dynamic scenes. Event cameras react to dynamic changes with higher temporal resolution (microsecond) and higher dynamic range (120dB), offering an alternative solution. In this work, we present a novel nighttime dynamic imaging method with an event camera. Specifically, we discover that the event at nighttime exhibits temporal trailing characteristics and spatial non-stationary distribution. Consequently, we propose a nighttime event reconstruction network (NER-Net) which mainly includes a learnable event timestamps calibration module (LETC) to align the temporal trailing events and a non-uniform illumination aware module (NIAM) to stabilize the spatiotemporal distribution of events. Moreover, we construct a paired real low-light event dataset (RLED) through a co-axial imaging system, including 64,200 spatially and temporally aligned image GTs and low-light events. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art methods in terms of visual quality and generalization ability on real-world nighttime datasets. The project are available at: https://github.com/Liu-haoyue/NER-Net.

Event-based moving object detection is a challenging task, where static background and moving object are mixed together. Typically, existing methods mainly align the background events to the same spatial coordinate system via motion compensation to distinguish the moving object. However, they neglect the potential spatial tailing effect of moving object events caused by excessive motion, which may affect the structure integrity of the extracted moving object. We discover that the moving object has a complete columnar structure in the point cloud composed of motion-compensated events along the timestamp. Motivated by this, we propose a novel joint spatio-temporal reasoning method for event-based moving object detection. Specifically, we first compensate the motion of background events using inertial measurement unit. In spatial reasoning stage, we project the compensated events into the same image coordinate, discretize the timestamp of events to obtain a time image that can reflect the motion confidence, and further segment the moving object through adaptive threshold on the time image. In temporal reasoning stage, we construct the events into a point cloud along timestamp, and use RANSAC algorithm to extract the columnar shape in the cloud for peeling off the background. Finally, we fuse the results from the two reasoning stages to extract the final moving object region. This joint spatio-temporal reasoning framework can effectively detect the moving object from motion confidence and geometric structure. Moreover, we conduct extensive experiments on various datasets to verify that the proposed method can improve the moving object detection accuracy by 13%.