A Novel Spike Transformer Network for Depth Estimation from Event Cameras via Cross-modality Knowledge Distillation

• URL: http://arxiv.org/abs/2404.17335v2
• Date: Wed, 1 May 2024 08:54:54 GMT
• Title: A Novel Spike Transformer Network for Depth Estimation from Event Cameras via Cross-modality Knowledge Distillation
• Authors: Xin Zhang, Liangxiu Han, Tam Sobeih, Lianghao Han, Darren Dancey,
• Abstract summary: Event cameras operate differently from traditional digital cameras, continuously capturing data and generating binary spikes that encode time, location, and light intensity. This necessitates the development of innovative, spike-aware algorithms tailored for event cameras. We propose a purely spike-driven spike transformer network for depth estimation from spiking camera data.
• Score: 3.355813093377501
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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