# Labeling Panoramas with Spherical Hourglass Networks

Source: [Labeling Panoramas with Spherical Hourglass Networks](<https://www.sota2.com/research/paper/68fa64c91e209131cea06a47>)

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

Data updated: 2025-10-27T05:55:49.570Z

Authors: Carlos Esteves, Kostas Daniilidis, Ameesh Makadia

Published: 2018-09-06T17:55:11.000Z

## Abstract

With the recent proliferation of consumer-grade 360{\\deg} cameras, it is worth revisiting visual perception challenges with spherical cameras given the potential benefit of their global field of view. To this end we introduce a spherical convolutional hourglass network (SCHN) for the dense labeling on the sphere. The SCHN is invariant to camera orientation (lifting the usual requirement for \`upright' panoramic images), and its design is scalable for larger practical datasets. Initial experiments show promising results on a spherical semantic segmentation task.

## 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/1809.02123>)

[PDF](<https://arxiv.org/pdf/1809.02123.pdf>)