6 papers
Adapting Depth Anything to Adverse Imaging Conditions with Events
Shihan Peng, Yuyang Xiong, Hanyu Zhou +5
Robust depth estimation under dynamic and adverse lighting conditions is essential for robotic systems. Currently, depth foundation models, such as Depth Anything, achieve great su…
4D-VGGT: A General Foundation Model with SpatioTemporal Awareness for Dynamic Scene Geometry Estimation
Haonan Wang, Hanyu Zhou, Haoyue Liu +1
We investigate a challenging task of dynamic scene geometry estimation, which requires representing both spatial and temporal features. Typically, existing methods align the two fe…
Injecting Frame-Event Complementary Fusion into Diffusion for Optical Flow in Challenging Scenes
Haonan Wang, Hanyu Zhou, Haoyue Liu +1
Optical flow estimation has achieved promising results in conventional scenes but faces challenges in high-speed and low-light scenes, which suffer from motion blur and insufficien…
STD-GS: Exploring Frame-Event Interaction for SpatioTemporal-Disentangled Gaussian Splatting to Reconstruct High-Dynamic Scene
Hanyu Zhou, Haonan Wang, Haoyue Liu +3
High-dynamic scene reconstruction aims to represent static background with rigid spatial features and dynamic objects with deformed continuous spatiotemporal features. Typically, e…
TimeTracker: Event-based Continuous Point Tracking for Video Frame Interpolation with Non-linear Motion
Haoyue Liu, Jinghan Xu, Yi Chang +4
Video frame interpolation (VFI) that leverages the bio-inspired event cameras as guidance has recently shown better performance and memory efficiency than the frame-based methods,…
Bridge Frame and Event: Common Spatiotemporal Fusion for High-Dynamic Scene Optical Flow
Hanyu Zhou, Haonan Wang, Haoyue Liu +3
High-dynamic scene optical flow is a challenging task, which suffers spatial blur and temporal discontinuous motion due to large displacement in frame imaging, thus deteriorating t…