Time Lens++: Event-based Frame Interpolation with Parametric Non-linear Flow and Multi-scale Fusion
This work solves frame interpolation for event-based cameras, offering incremental improvements in quality and efficiency for real-world scenes.
The paper tackled video frame interpolation by addressing brittle fusion, inefficient motion estimation, and low-contrast artifacts in event-based methods, resulting in improvements of up to 0.2 dB in PSNR and 15% in LPIPS score.
Recently, video frame interpolation using a combination of frame- and event-based cameras has surpassed traditional image-based methods both in terms of performance and memory efficiency. However, current methods still suffer from (i) brittle image-level fusion of complementary interpolation results, that fails in the presence of artifacts in the fused image, (ii) potentially temporally inconsistent and inefficient motion estimation procedures, that run for every inserted frame and (iii) low contrast regions that do not trigger events, and thus cause events-only motion estimation to generate artifacts. Moreover, previous methods were only tested on datasets consisting of planar and faraway scenes, which do not capture the full complexity of the real world. In this work, we address the above problems by introducing multi-scale feature-level fusion and computing one-shot non-linear inter-frame motion from events and images, which can be efficiently sampled for image warping. We also collect the first large-scale events and frames dataset consisting of more than 100 challenging scenes with depth variations, captured with a new experimental setup based on a beamsplitter. We show that our method improves the reconstruction quality by up to 0.2 dB in terms of PSNR and up to 15% in LPIPS score.