# Optical Flow with Semantic Segmentation and Localized Layers Source: [Optical Flow with Semantic Segmentation and Localized Layers]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:19:32.265Z Authors: Laura Sevilla-Lara, Deqing Sun, Varun Jampani, Michael J. Black Published: 2016-03-12T13:34:09.000Z ## Abstract Existing optical flow methods make generic, spatially homogeneous, assumptions about the spatial structure of the flow. In reality, optical flow varies across an image depending on object class. Simply put, different objects move differently. Here we exploit recent advances in static semantic scene segmentation to segment the image into objects of different types. We define different models of image motion in these regions depending on the type of object. For example, we model the motion on roads with homographies, vegetation with spatially smooth flow, and independently moving objects like cars and planes with affine motion plus deviations. We then pose the flow estimation problem using a novel formulation of localized layers, which addresses limitations of traditional layered models for dealing with complex scene motion. Our semantic flow method achieves the lowest error of any published monocular method in the KITTI-2015 flow benchmark and produces qualitatively better flow and segmentation than recent top methods on a wide range of natural videos. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Optical Flow on KITTI 2015 (test)](): Optical Flow; KITTI 2015 (test); Fl Error (All): 15.99. ## Original sources and implementation [Original paper]() [PDF]()