The paper proposes a novel spike transformer network for depth estimation from event camera data. The key highlights are:
The network incorporates spike-driven residual learning and spike self-attention mechanisms to eliminate the need for floating-point and integer-float multiplications, adhering to the principled spike-based operation and significantly reducing energy consumption.
A comprehensive single-stage knowledge distillation framework is developed, deriving insights from both the final and intermediate layers of the large vision foundation model (DINOv2) to effectively transfer knowledge to the spiking neural network (SNN) and facilitate efficient training on limited datasets.
Thorough experimental evaluation on both real and synthetic datasets demonstrates that the proposed method reliably predicts depth maps and outperforms competing methods by a significant margin, with notable gains in Absolute Relative and Square Relative errors.
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ข้อมูลเชิงลึกที่สำคัญจาก
by Xin Zhang,Li... ที่ arxiv.org 04-29-2024
https://arxiv.org/pdf/2404.17335.pdfสอบถามเพิ่มเติม