Blocks: Collaborative and Persistent Augmented Reality Experiences
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Abstract (arXiv)
We introduce Blocks, a mobile application that enables people to co-create AR structures that persist in the physical environment. Using Blocks, end users can collaborate synchronously or asynchronously, whether they are colocated or remote. Additionally, the AR structures can be tied to a physical location or can be accessed from anywhere. We evaluated how people used Blocks through a series of lab and field deployment studies with over 160 participants, and explored the interplay between two collaborative dimensions: space and time. We found that participants preferred creating structures synchronously with colocated collaborators. Additionally, they were most active when they created structures that were not restricted by time or place. Unlike most of today's AR experiences, which focus on content consumption, this work outlines new design opportunities for persistent and collaborative AR experiences that empower anyone to collaborate and create AR content.
Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). First posted on arXiv 7 Aug 2019.
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Citing papers per year: 2023: 2; 2024: 4; 2025: 4; 2026: 1.
11 papers.
- 2024VisualPredicator: Learning Abstract World Models with Neuro-Symbolic Predicates for Robot Planningcited by 39
- 2023Machine Learning for Large-Scale Optimization in 6G Wireless Networkscited by 18
- 2023Out of Context: Investigating the Bias and Fairness Concerns of "Artificial Intelligence as a Service"cited by 15
- 2024QuantMoE-Bench: Examining Post-Training Quantization for Mixture-of-Expertscited by 9
- 2025MAPS: Preserving Vision-Language Representations via Module-Wise Proximity Scheduling for Better Vision-Language-Action Generalizationcited by 7
- 2025Coupled Diffusion Sampling for Training-Free Multi-View Image Editingcited by 4
- 2024Deep Learning for Generalised Planning with Background Knowledgecited by 2
- 2024BA-Net: Bridge Attention in Deep Neural Networkscited by 0
- 2025Prediction of Permissioned Blockchain Performance for Resource Scaling Configurationscited by 0
- 2025Energy-Based Models for Predicting Mutational Effects on Proteinscited by 0
- 2026Two Dimensions Govern Agnostic Multiclass Transductive Learningcited by 0
- 2026Two Dimensions Govern Agnostic Multiclass Transductive Learning26 Aug 2026
- 2025MAPS: Preserving Vision-Language Representations via Module-Wise Proximity Scheduling for Better Vision-Language-Action Generalization25 Nov 2025
- 2025Coupled Diffusion Sampling for Training-Free Multi-View Image Editing16 Oct 2025
- 2025Energy-Based Models for Predicting Mutational Effects on Proteins14 Aug 2025
- 2025Prediction of Permissioned Blockchain Performance for Resource Scaling Configurations20 Mar 2025
- 2024VisualPredicator: Learning Abstract World Models with Neuro-Symbolic Predicates for Robot Planning30 Oct 2024
- 2024Deep Learning for Generalised Planning with Background Knowledge10 Oct 2024
- 2024BA-Net: Bridge Attention in Deep Neural Networks10 Oct 2024
- 2024QuantMoE-Bench: Examining Post-Training Quantization for Mixture-of-Experts12 Jun 2024
- 2023Out of Context: Investigating the Bias and Fairness Concerns of "Artificial Intelligence as a Service"2 Feb 2023
- 2023Machine Learning for Large-Scale Optimization in 6G Wireless Networks3 Jan 2023