PixRO: Pixel-Distributed Rotational Odometry with Gaussian Belief Propagation
This work addresses inefficiencies in energy and computation for computer vision tasks by shifting processing to in-pixel sensors, though it is incremental as it applies an existing probabilistic method to a new distributed setting.
The paper tackles the problem of inefficient image processing by proposing a pixel-distributed rotational odometry algorithm that estimates camera motion directly at the pixel level using Gaussian Belief Propagation, achieving competitive performance on real-world datasets.
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.