# Spatial Sampling Network for Fast Scene Understanding Source: [Spatial Sampling Network for Fast Scene Understanding]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:35:37.665Z Authors: Davide Mazzini, Raimondo Schettini Published: 2019-05-22T09:24:17.000Z ## Abstract We propose a network architecture to perform efficient scene understanding. This work presents three main novelties: the first is an Improved Guided Upsampling Module that can replace in toto the decoder part in common semantic segmentation networks. Our second contribution is the introduction of a new module based on spatial sampling to perform Instance Segmentation. It provides a very fast instance segmentation, needing only thresholding as post-processing step at inference time. Finally, we propose a novel efficient network design that includes the new modules and test it against different datasets for outdoor scene understanding. To our knowledge, our network is one of the themost efficient architectures for scene understanding published to date, furthermore being 8.6% more accurate than the fastest competitor on semantic segmentation and almost five times faster than the most efficient network for instance segmentation. ## 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. - [Instance Segmentation on Cityscapes (test)](): Instance Segmentation; Cityscapes (test); AP (Overall): 9.2. ## Original sources and implementation [Original paper]() [PDF]()