# PanoTree: Autonomous Photo-Spot Explorer in Virtual Reality Scenes

arXiv: https://arxiv.org/abs/2405.17136 · HTML: https://mlsanity.com/paper/2405.17136/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 52 references that are in the map.
- First posted on arXiv 27 May 2024.

## Builds on (most cited first, 21 of 21)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [RandAugment: Practical automated data augmentation with a reduced search space](https://mlsanity.com/paper/1909.13719/) (2019)
- [MLP-Mixer: An all-MLP Architecture for Vision](https://mlsanity.com/paper/2105.01601/) (2021)
- [Magic3D: High-Resolution Text-to-3D Content Creation](https://mlsanity.com/paper/2211.10440/) (2022)
- [Random Erasing Data Augmentation](https://mlsanity.com/paper/1708.04896/) (2017)
- [When Does Label Smoothing Help?](https://mlsanity.com/paper/1906.02629/) (2019)
- [DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation](https://mlsanity.com/paper/2309.16653/) (2023)
- [Image Segmentation Using Deep Learning: A Survey](https://mlsanity.com/paper/2001.05566/) (2020)
- [BARF: Bundle-Adjusting Neural Radiance Fields](https://mlsanity.com/paper/2104.06405/) (2021)
- [Semantic Scene Completion from a Single Depth Image](https://mlsanity.com/paper/1611.08974/) (2016)
- [Dream3D: Zero-Shot Text-to-3D Synthesis Using 3D Shape Prior and Text-to-Image Diffusion Models](https://mlsanity.com/paper/2212.14704/) (2022)
- [CamP: Camera Preconditioning for Neural Radiance Fields](https://mlsanity.com/paper/2308.10902/) (2023)
- [Effective Aesthetics Prediction with Multi-level Spatially Pooled Features](https://mlsanity.com/paper/1904.01382/) (2019)
- [Image Aesthetic Assessment: An Experimental Survey](https://mlsanity.com/paper/1610.00838/) (2016)
- [LLMR: Real-time Prompting of Interactive Worlds using Large Language Models](https://mlsanity.com/paper/2309.12276/) (2023)
- [On the Global Convergence of Particle Swarm Optimization Methods](https://mlsanity.com/paper/2201.12460/) (2022)
- [Steps towards prompt-based creation of virtual worlds](https://mlsanity.com/paper/2211.05875/) (2022)

## Built on by (most cited first, 1 of 1)

- [Large language models for artificial general intelligence (AGI): A survey of foundational principles and approaches](https://mlsanity.com/paper/2501.03151/) (2025)

## Abstract (arXiv)

Social VR platforms enable social, economic, and creative activities by allowing users to create and share their own virtual spaces. In social VR, photography within a VR scene is an important indicator of visitors' activities. Although automatic identification of photo spots within a VR scene can facilitate the process of creating a VR scene and enhance the visitor experience, there are challenges in quantitatively evaluating photos taken in the VR scene and efficiently exploring the large VR scene. We propose PanoTree, an automated photo-spot explorer in VR scenes. To assess the aesthetics of images captured in VR scenes, a deep scoring network is trained on a large dataset of photos collected by a social VR platform to determine whether humans are likely to take similar photos. Furthermore, we propose a Hierarchical Optimistic Optimization (HOO)-based search algorithm to efficiently explore 3D VR spaces with the reward from the scoring network. Our user study shows that the scoring network achieves human-level performance in distinguishing randomly taken images from those taken by humans. In addition, we show applications using the explored photo spots, such as automatic thumbnail generation, support for VR world creation, and visitor flow planning within a VR scene.