BrickStARt: Enabling In-situ Design and Tangible Exploration for Personal Fabrication using Mixed Reality
October 05, 2023 Β· Declared Dead Β· π Proc. ACM Hum. Comput. Interact.
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Evgeny Stemasov, Jessica Hohn, Maurice Cordts, Anja Schikorr, Enrico Rukzio, Jan Gugenheimer
arXiv ID
2310.03700
Category
cs.HC: Human-Computer Interaction
Citations
7
Venue
Proc. ACM Hum. Comput. Interact.
Last Checked
4 months ago
Abstract
3D printers enable end-users to design and fabricate unique physical artifacts but maintain an increased entry barrier and friction. End users must design tangible artifacts through intangible media away from the main problem space (ex-situ) and transfer spatial requirements to an abstract software environment. To allow users to evaluate dimensions, balance, or fit early and in-situ, we developed BrickStARt, a design tool using tangible construction blocks paired with a mixed-reality headset. Users assemble a physical block model at the envisioned location of the fabricated artifact. Designs can be tested tangibly, refined, and digitally post-processed, remaining continuously in-situ. We implemented BrickStARt using a Magic Leap headset and present walkthroughs, highlighting novel interactions for 3D design. In a user study (n=16), first-time 3D modelers succeeded more often using BrickStARt than Tinkercad. Our results suggest that BrickStARt provides an accessible and explorative process while facilitating quick, tangible design iterations that allow users to detect physics-related issues (e.g., clearance) early on.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted