像素级分布式视觉里程计与深度估计，提升传感器端计算效率
PixVOD: Pixel-Distributed Direct Visual Odometry and Depth Estimation
- 像素内并行计算，通过高斯信念传播协同求解运动与深度
- 引入关键帧锚定机制，保持几何稳定性，实现连续更新
- 适用于低功耗嵌入式视觉系统，适合边缘智能场景
图像由二维像素数组构成，是计算机视觉算法的标准输入，但许多底层计算可分布于各像素中。将原始、冗余且噪声大的像素数据从传感器传出仍效率低下，因此需要转向在焦平面处集成处理器的传感器，使大量计算在像素内直接完成。我们设想像素可本地合成高层信号，减轻下游计算负担，并为高级视觉任务提供更丰富的输入。本文提出一种完全可并行化的像素级视觉里程计与深度估计方法，传感器处理器通过高斯信念传播（GBP）交换信息，达成对相机运动的一致性判断，并基于每像素的光度观测与表面法向先验推断深度。为在优化过程中维持几何稳定性，我们引入类似关键帧的锚定机制，调控帧间有效基线，从而实现一致的运动与深度更新。该方法在真实数据集上进行了评估，证明了基于GBP的像素级分布式里程计与深度估计结合关键帧锚定在传感器端的可行性。
原文摘要 · Abstract (English)
Images composed of 2D pixel arrays are the standard input to computer vision algorithms, yet many underlying computations can be distributed across pixels. Transmitting raw, redundant, and noisy pixel data off the sensor remains inefficient, motivating a shift toward focal-plane sensor-processors that perform a significant part of the computation directly within each pixel. We envision pixels synthesizing higher-level signals locally, reducing downstream load, and providing richer inputs for higher-level vision tasks. We propose a fully parallelizable form of visual odometry and depth estimation across pixels, where sensor-processors exchange information through Gaussian Belief Propagation (GBP) to achieve consensus about camera motion and infer depth from per-pixel photometric observations and a surface normal prior. To maintain geometric stability during optimization, we introduce a keyframe-like anchoring mechanism that regulates the effective baseline between frames, enabling consistent motion and depth updates. Our method is evaluated on realistic datasets, demonstrating the feasibility of GBP-based pixel-level distributed odometry and depth estimation with keyframe anchoring on-sensor. Project Page: https://www.shinjeongkim.com/pixvod/
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