# What Can I Do Around Here? Deep Functional Scene Understanding for Cognitive Robots

Source: [What Can I Do Around Here? Deep Functional Scene Understanding for Cognitive Robots](<https://www.sota2.com/research/paper/68fa62441e209131ce9f8578>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:16:31.033Z

Authors: Chengxi Ye, Yezhou Yang, Cornelia Fermuller, Yiannis Aloimonos

Published: 2016-01-29T22:55:53.000Z

## Abstract

For robots that have the capability to interact with the physical environment through their end effectors, understanding the surrounding scenes is not merely a task of image classification or object recognition. To perform actual tasks, it is critical for the robot to have a functional understanding of the visual scene. Here, we address the problem of localizing and recognition of functional areas from an arbitrary indoor scene, formulated as a two-stage deep learning based detection pipeline. A new scene functionality testing-bed, which is complied from two publicly available indoor scene datasets, is used for evaluation. Our method is evaluated quantitatively on the new dataset, demonstrating the ability to perform efficient recognition of functional areas from arbitrary indoor scenes. We also demonstrate that our detection model can be generalized onto novel indoor scenes by cross validating it with the images from two different datasets.

## 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.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1602.00032>)

[PDF](<https://arxiv.org/pdf/1602.00032.pdf>)