Depth estimation is crucial for interpreting complex environments, especially
in areas such as autonomous vehicle navigation and robotics. Nonetheless,
obtaining accurate depth readings from event camera data remains a formidable
challenge. Event cameras operate differently from traditional digital cameras,
continuously capturing data and generating asynchronous binary spikes that
encode time, location, and light intensity. Yet, the unique sampling mechanisms
of event cameras render standard image based algorithms inadequate for
processing spike data. This necessitates the development of innovative,
spike-aware algorithms tailored for event cameras, a task compounded by the
irregularity, continuity, noise, and spatial and temporal characteristics
inherent in spiking data.Harnessing the strong generalization capabilities of
transformer neural networks for spatiotemporal data, we propose a purely
spike-driven spike transformer network for depth estimation from spiking camera
data. To address performance limitations with Spiking Neural Networks (SNN), we
introduce a novel single-stage cross-modality knowledge transfer framework
leveraging knowledge from a large vision foundational model of artificial
neural networks (ANN) (DINOv2) to enhance the performance of SNNs with limited
data. Our experimental results on both synthetic and real datasets show
substantial improvements over existing models, with notable gains in Absolute
Relative and Square Relative errors (49% and 39.77% improvements over the
benchmark model Spike-T, respectively). Besides accuracy, the proposed model
also demonstrates reduced power consumptions, a critical factor for practical
applications.

基于脉冲相机数据的纯脉冲驱动脉冲变压器网络模型以及跨模态知识迁移框架，在深度估计领域取得了显著提升的结果，同时减少了功耗消耗。