# 360-Indoor: Towards Learning Real-World Objects in 360deg Indoor Equirectangular Images

arXiv: https://arxiv.org/abs/1910.01712 · HTML: https://mlsanity.com/paper/1910.01712/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2019.

## Built on by (most cited first, 26 of 26)

- [Deep Learning for Omnidirectional Vision: A Survey and New Perspectives](https://mlsanity.com/paper/2205.10468/) (2022)
- [DeepPanoContext: Panoramic 3D Scene Understanding with Holistic Scene Context Graph and Relation-based Optimization](https://mlsanity.com/paper/2108.10743/) (2021)
- [Eye, Robot: Learning to Look to Act with a BC-RL Perception-Action Loop](https://mlsanity.com/paper/2506.10968/) (2025)
- [A Survey of Representation Learning, Optimization Strategies, and Applications for Omnidirectional Vision](https://mlsanity.com/paper/2502.10444/) (2025)
- [PanoSwin: a Pano-style Swin Transformer for Panorama Understanding](https://mlsanity.com/paper/2308.14726/) (2023)
- [Cylin-Painting: Seamless 360textdegree Panoramic Image Outpainting and Beyond](https://mlsanity.com/paper/2204.08563/) (2022)
- [AirSim360: A Panoramic Simulation Platform within Drone View](https://mlsanity.com/paper/2512.02009/) (2025)
- [Spherical Transformer](https://mlsanity.com/paper/2202.04942/) (2022)
- [360VOT: A New Benchmark Dataset for Omnidirectional Visual Object Tracking](https://mlsanity.com/paper/2307.14630/) (2023)
- [Geometry Fidelity for Spherical Images](https://mlsanity.com/paper/2407.18207/) (2024)
- [360VOTS: Visual Object Tracking and Segmentation in Omnidirectional Videos](https://mlsanity.com/paper/2404.13953/) (2024)
- [Unbiased IoU for Spherical Image Object Detection](https://mlsanity.com/paper/2108.08029/) (2021)
- [SHD360: A Benchmark Dataset for Salient Human Detection in 360deg Videos](https://mlsanity.com/paper/2105.11578/) (2021)
- [ASOD60K: An Audio-Induced Salient Object Detection Dataset for Panoramic Videos](https://mlsanity.com/paper/2107.11629/) (2021)
- [SelectionConv: Convolutional Neural Networks for Non-rectilinear Image Data](https://mlsanity.com/paper/2207.08979/) (2022)
- [Field-of-View IoU for Object Detection in 360deg Images](https://mlsanity.com/paper/2202.03176/) (2022)
- [Fire360: A Benchmark for Robust Perception and Episodic Memory in Degraded 360-Degree Firefighting Videos](https://mlsanity.com/paper/2506.02167/) (2025)
- [MINERVAS: Massive INterior EnviRonments VirtuAl Synthesis](https://mlsanity.com/paper/2107.06149/) (2021)
- [Spatial and Semantic Embedding Integration for Stereo Sound Event Localization and Detection in Regular Videos](https://mlsanity.com/paper/2507.04845/) (2025)
- [Integrating Spatial and Semantic Embeddings for Stereo Sound Event Localization in Videos](https://mlsanity.com/paper/2509.06598/) (2025)
- [EAGLE-360: Embodied Active Global-to-Local Exploration in 360^circ](https://mlsanity.com/paper/2607.02479/) (2026)
- [Review Neural Networks about Image Transformation Based on IGC Learning Framework with Annotated Information](https://mlsanity.com/paper/2206.10155/) (2022)
- [FindView: Precise Target View Localization Task for Look Around Agents](https://mlsanity.com/paper/2303.09054/) (2023)
- [OmniSense: Towards Edge-Assisted Online Analytics for 360-Degree Videos](https://mlsanity.com/paper/2508.14237/) (2025)
- [Seek to Segment: Active Perception for Panoramic Referring Segmentation](https://mlsanity.com/paper/2607.02497/) (2026)
- [PanoPed: Beyond Bounding Boxes for Sim-to-Real Panoramic Pedestrian Tracking](https://mlsanity.com/paper/2610.08826/) (2026)

## Abstract (arXiv)

While there are several widely used object detection datasets, current computer vision algorithms are still limited in conventional images. Such images narrow our vision in a restricted region. On the other hand, 360deg images provide a thorough sight. In this paper, our goal is to provide a standard dataset to facilitate the vision and machine learning communities in 360deg domain. To facilitate the research, we present a real-world 360deg panoramic object detection dataset, 360-Indoor, which is a new benchmark for visual object detection and class recognition in 360deg indoor images. It is achieved by gathering images of complex indoor scenes containing common objects and the intensive annotated bounding field-of-view. In addition, 360-Indoor has several distinct properties: (1) the largest category number (37 labels in total). (2) the most complete annotations on average (27 bounding boxes per image). The selected 37 objects are all common in indoor scene. With around 3k images and 90k labels in total, 360-Indoor achieves the largest dataset for detection in 360deg images. In the end, extensive experiments on the state-of-the-art methods for both classification and detection are provided. We will release this dataset in the near future.