Event cameras are dynamic vision sensors inspired by the biological retina, offering high dynamic range, high temporal resolution, and low power consumption. These qualities allow them to perceive 3D environments even in extreme conditions. Event data is continuously recorded over time, capturing pixel movements in detail. To leverage this temporal density, we introduce a temporal event stereo framework that continuously uses past information. The event stereo matching network is jointly trained with stereoscopic flow, which tracks pixel movements from stereo cameras. Instead of relying on optical flow ground truth, our method trains motion flows using disparity maps. The temporal aggregation of information via stereoscopic flow boosts stereo matching performance, achieving state-of-the-art results on MVSEC, DSEC, M3ED, and EVIMO2 datasets. Our method also demonstrates computational efficiency by stacking past data in a cascading manner.

Detecting 3D objects in point clouds plays a crucial role in autonomous driving systems. Recently, advanced multimodal methods incorporating camera information have achieved notable performance. For a safe and effective autonomous driving system, algorithms that excel not only in accuracy but also in speed and low latency are essential. However, existing algorithms fail to meet these requirements due to the latency and bandwidth limitations of fixed frame rate sensors, e.g., LiDAR and camera. To address this limitation, we introduce asynchronous event cameras into 3D object detection for the first time. We leverage their high temporal resolution and low bandwidth to enable high-speed 3D object detection. Our method enables detection even during inter-frame intervals when synchronized data is unavailable, by retrieving previous 3D information through the event camera. Furthermore, we introduce the first event-based 3D object detection datasets, Ev-Waymo and DSEC-3DOD, both of which include ground truth 3D bounding boxes at 100 FPS, establishing the first benchmarks for event-based 3D detectors. The code and dataset are available at https://github.com/mickeykang16/Ev3DOD.

3D object detection is essential for autonomous systems, enabling precise localization and dimension estimation. While LiDAR and RGB cameras are widely used, their fixed frame rates create perception gaps in high-speed scenarios. Event cameras, with their asynchronous nature and high temporal resolution, offer a solution by capturing motion continuously. The recent approach, which integrates event cameras with conventional sensors for continuous-time detection, struggles in fast-motion scenarios due to its dependency on synchronized sensors. We propose a novel stereo 3D object detection framework that relies solely on event cameras, eliminating the need for conventional 3D sensors. To compensate for the lack of semantic and geometric information in event data, we introduce a dual filter mechanism that extracts both. Additionally, we enhance regression by aligning bounding boxes with object-centric information. Experiments show that our method outperforms prior approaches in dynamic environments, demonstrating the potential of event cameras for robust, continuous-time 3D perception. The code is available at https://github.com/mickeykang16/Ev-Stereo3D.

End-to-end autonomous driving (E2E-AD) has emerged as a promising paradigm that unifies perception, prediction, and planning into a holistic, data-driven framework. However, achieving robustness to varying camera viewpoints, a common real-world challenge due to diverse vehicle configurations, remains an open problem. In this work, we propose VR-Drive, a novel E2E-AD framework that addresses viewpoint generalization by jointly learning 3D scene reconstruction as an auxiliary task to enable planning-aware view synthesis. Unlike prior scene-specific synthesis approaches, VR-Drive adopts a feed-forward inference strategy that supports online training-time augmentation from sparse views without additional annotations. To further improve viewpoint consistency, we introduce a viewpoint-mixed memory bank that facilitates temporal interaction across multiple viewpoints and a viewpoint-consistent distillation strategy that transfers knowledge from original to synthesized views. Trained in a fully end-to-end manner, VR-Drive effectively mitigates synthesis-induced noise and improves planning under viewpoint shifts. In addition, we release a new benchmark dataset to evaluate E2E-AD performance under novel camera viewpoints, enabling comprehensive analysis. Our results demonstrate that VR-Drive is a scalable and robust solution for the real-world deployment of end-to-end autonomous driving systems.

Event cameras deliver asynchronous pixel intensity changes, which result in sparse event data that offers the advantages of high temporal resolution. These high temporal characteristics make researchers naturally incorporate event cameras into video frame interpolation (VFI) and video super-resolution (VSR). In this paper, we make the first attempt to solve the space-time video super-resolution (STVSR) task effectively, addressing both VFI and VSR simultaneously, by leveraging temporally dense events. STVSR aims to generate intermediate high-resolution (HR) videos between consecutive low-resolution (LR) frames. To fully exploit the high temporal frequency of events for STVSR, we focus on temporal alignment in two stages, at low-resolution and after up-sampling in high-resolution. In temporal alignment at low-resolution, to upsample spatial dimensions effectively, we leverage high temporal features to preserve spatial context. On the other hand, for temporal alignment at the high-resolution stage, we employ a deformable sampling process from events to achieve accurate alignment with forward and backward directions. In addition, we provide the SuperREST dataset, which features high-frequency details and complex motion in an RGB-Event setup. Experimental results on several datasets demonstrate that our method achieves a significant performance gain on STVSR tasks with low computational cost. Our codes and datasets are available at h t t ps: //github.com/Chohoonhee/ESTNet.