PixVOD: Pixel-Distributed Direct Visual Odometry and Depth Estimation

Images composed of 2D pixel arrays are the standard inputto computer vision algorithms, yet many underlying computations can bedistributed across pixels. Transmitting raw, redundant, and noisy pixeldata off the sensor remains inefficient, motivating a shift toward focal-plane sensor-processors that perform a significant part of the computa-tion directly within each pixel. We envision pixels synthesizing higher-level signals locally, reducing downstream load, and providing richer in-puts for higher-level vision tasks.We propose a fully parallelizable form of visual odometry and depthestimation across pixels, where sensor-processors exchange informationthrough Gaussian Belief Propagation (GBP) to achieve consensus aboutcamera motion and infer depth from per-pixel photometric observationsand a surface normal prior. To maintain geometric stability during op-timization, we introduce a keyframe-like anchoring mechanism that reg-ulates the effective baseline between frames, enabling consistent mo-tion and depth updates. Our method is evaluated on realistic datasets,demonstrating the feasibility of GBP-based pixel-level distributed odom-etry and depth estimation with keyframe anchoring on-sensor. ProjectPage: https://www.shinjeongkim.com/pixvod/