PixRO: 基于高斯信念传播的像素分布式旋转里程计
PixRO: Pixel-Distributed Rotational Odometry with Gaussian Belief Propagation
AI总结：
针对原始像素读数在传输和处理中的低效问题，本文提出PixRO算法，在像素级分布式部署基于高斯信念传播的光度旋转估计，通过像素间信息交换达成全局共识，以减少下游计算开销并支持下游任务。
摘要：
图像是大多数计算机视觉算法的标准输入。然而，其处理通常简化为对单个像素进行局部且独立的并行化操作。但对于特定高级任务而言，许多这些低级原始像素读数仅提供冗余或含噪信息，导致传感器外传输时的能耗和后续处理中的计算资源均存在低效问题。随着具备先进像素内处理能力的新型传感器出现，我们设想一种范式转变：直接在像素内执行日益复杂的视觉处理，以减少下游计算开销。我们主张在像素级合成高级线索，使其传感器外传输比原始像素读数更有效地直接支持下游任务。本文提出一种新型光度旋转估计算法，将其分布式部署于像素级，其中每个像素通过与其他像素交换信息达成全局共识来估计相机的全局运动。我们采用概率公式，并利用高斯信念传播（GBP）通过消息传递进行去中心化推理。所提技术在真实世界公共数据集上进行了评估，我们还深入分析了将GBP应用于像素级分布式旋转估计的实用性。
英文摘要：
Images are the standard input for most computer vision algorithms. However, their processing often reduces to parallelizable operations applied locally and independently to individual pixels. Yet, many of these low-level raw pixel readings only provide redundant or noisy information for specific high-level tasks, leading to inefficiencies in both energy consumption during their transmission off-sensor and computational resources in their subsequent processing. As novel sensors featuring advanced in-pixel processing capabilities emerge, we envision a paradigm shift toward performing increasingly complex visual processing directly in-pixel, reducing computational overhead downstream. We advocate for synthesizing high-level cues at the pixel level, enabling their off-sensor transmission to directly support downstream tasks more effectively than raw pixel readings. This paper conceptualizes a novel photometric rotation estimation algorithm to be distributed at pixel level, where each pixel estimates the global motion of the camera by exchanging information with other pixels to achieve global consensus. We employ a probabilistic formulation and leverage Gaussian Belief Propagation (GBP) for decentralized inference using messaging-passing. The proposed proposed technique is evaluated on real-world public datasets and we offer a in-depth analysis of the practicality of applying GBP to distributed rotation estimation at pixel level.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
