Non-fusion time-resolved depth image reconstruction using a highly efficient neural network architecture.
Non-fusion time-resolved depth image reconstruction using a highly efficient neural network architecture.
复制标题
使用高效神经网络架构进行非融合时间分辨深度图像重建。
作者：
Zhenya Zang;Dongjie Xiao;David Day
单光子雪崩二极管（SPAD）由于其单光子灵敏度，是用于弱光场景中的3D光探测和测距（LiDAR）的强大传感器。然而，准确地从噪声到达时间（ToA）点云检索测距信息仍然是一个挑战。本文提出了一种光子高效的非融合神经网络架构，可以直接从ToA数据重建高保真深度图像，而不依赖于其他指导图像。此外，通过低比特量化方案对神经网络结构进行压缩，使其适合在嵌入式硬件平台上实现。与之前报道的网络相比，所提出的量化神经网络架构实现了更高的上级重建精度和更少的参数。
Single-photon avalanche diodes (SPAD) are powerful sensors for 3D light detection and ranging (LiDAR) in low light scenarios due to their single-photon sensitivity. However, accurately retrieving ranging information from noisy time-of-arrival (ToA) point clouds remains a challenge. This paper proposes a photon-efficient, non-fusion neural network architecture that can directly reconstruct high-fidelity depth images from ToA data without relying on other guiding images. Besides, the neural network architecture was compressed via a low-bit quantization scheme so that it is suitable to be implemented on embedded hardware platforms. The proposed quantized neural network architecture achieves superior reconstruction accuracy and fewer parameters than previously reported networks.
影响因子：
3.8
作者：
Ruget, Alice;McLaughlin, Stephen;Leach, Jonathan
通讯作者：
Leach, Jonathan