用最少像素实现高效视觉，自由形状传感器提升信息密度。
Minimalist Vision with Freeform Pixels
- 用自由形状光敏单元替代传统像素，形成可训练的感知层
- 8个自由像素即可完成监控、照明测量与交通流估算，性能媲美千倍像素相机
- 低功耗自供电设计，适合隐私保护场景
极简视觉系统仅使用完成任务所需的最少像素。与传统方形网格像素相机不同，极简相机采用可任意形状的自由像素以提高信息密度。我们发现，极简相机的硬件可建模为神经网络的第一层，后续层用于推理。针对特定任务训练网络后，可得到自由像素的最优形状，每个像素由光电探测器和光学掩膜实现。我们设计了用于室内空间监控（8像素）、房间光照测量（8像素）和交通流估计（8像素）的极简相机。这些系统性能与传统相机在千倍以上像素下相当。极简视觉有两个主要优势：一是由于捕捉信息不足以还原细节，天然具备隐私保护能力；二是因测量次数极少，可完全自供电，无需外部电源或电池。
原文摘要 · Abstract (English)
A minimalist vision system uses the smallest number of pixels needed to solve a vision task. While traditional cameras use a large grid of square pixels, a minimalist camera uses freeform pixels that can take on arbitrary shapes to increase their information content. We show that the hardware of a minimalist camera can be modeled as the first layer of a neural network, where the subsequent layers are used for inference. Training the network for any given task yields the shapes of the camera's freeform pixels, each of which is implemented using a photodetector and an optical mask. We have designed minimalist cameras for monitoring indoor spaces (with 8 pixels), measuring room lighting (with 8 pixels), and estimating traffic flow (with 8 pixels). The performance demonstrated by these systems is on par with a traditional camera with orders of magnitude more pixels. Minimalist vision has two major advantages. First, it naturally tends to preserve the privacy of individuals in the scene since the captured information is inadequate for extracting visual details. Second, since the number of measurements made by a minimalist camera is very small, we show that it can be fully self-powered, i.e., function without an external power supply or a battery.
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