Paper ID: 2304.04687

Learning to Detect Touches on Cluttered Tables

Norberto Adrian Goussies, Kenji Hata, Shruthi Prabhakara, Abhishek Amit, Tony Aube, Carl Cepress, Diana Chang, Li-Te Cheng, Horia Stefan Ciurdar, Mike Cleron, Chelsey Fleming, Ashwin Ganti, Divyansh Garg, Niloofar Gheissari, Petra Luna Grutzik, David Hendon, Daniel Iglesia, Jin Kim, Stuart Kyle, Chris LaRosa, Roman Lewkow, Peter F McDermott, Chris Melancon, Paru Nackeeran, Neal Norwitz, Ali Rahimi, Brett Rampata, Carlos Sobrinho, George Sung, Natalie Zauhar, Palash Nandy

We present a novel self-contained camera-projector tabletop system with a lamp form-factor that brings digital intelligence to our tables. We propose a real-time, on-device, learning-based touch detection algorithm that makes any tabletop interactive. The top-down configuration and learning-based algorithm makes our method robust to the presence of clutter, a main limitation of existing camera-projector tabletop systems. Our research prototype enables a set of experiences that combine hand interactions and objects present on the table. A video can be found at https://youtu.be/hElC_c25Fg8.

Submitted: Apr 10, 2023