# What's in my Room? Object Recognition on Indoor Panoramic Images

Source: [What's in my Room? Object Recognition on Indoor Panoramic Images](<https://www.sota2.com/research/paper/68fa694b1e209131cea2147e>)

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

Data updated: 2025-10-25T22:57:51.173Z

Authors: Julia Guerrero-Viu, Clara Fernandez-Labrador, C\\'edric Demonceaux, Jose J. Guerrero

Published: 2019-10-14T13:40:45.000Z

## Abstract

In the last few years, there has been a growing interest in taking advantage of the 360 panoramic images potential, while managing the new challenges they imply. While several tasks have been improved thanks to the contextual information these images offer, object recognition in indoor scenes still remains a challenging problem that has not been deeply investigated. This paper provides an object recognition system that performs object detection and semantic segmentation tasks by using a deep learning model adapted to match the nature of equirectangular images. From these results, instance segmentation masks are recovered, refined and transformed into 3D bounding boxes that are placed into the 3D model of the room. Quantitative and qualitative results support that our method outperforms the state of the art by a large margin and show a complete understanding of the main objects in indoor scenes.

## 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/1910.06138>)

[PDF](<https://arxiv.org/pdf/1910.06138.pdf>)