Fill and Transfer: A Simple Physics-based Approach for Containability Reasoning

Lap-Fai Yu1

Noah Duncan2

Sai-Kit Yeung3

1University of Massachusetts Boston

2University of California, Los Angeles

3Singapore University of Technology and Design


(a) Which direction shall we tilt the pitcher to pour the water out?
(b) Shall we use this container to hold water?
(c) Which object is a good liquid container?
(d) Shall we use a plate or a cup to carry water?

Abstract The visual perception of object affordances has emerged as a useful ingredient for building powerful computer vision and robotic applications [30]. In this paper we introduce a novel approach to reason about liquid containability - the affordance of containing liquid. Our approach analyzes container objects based on two simple physical processes: the Fill and Transfer of liquid. First, it reasons about whether a given 3D object is a liquid container and its best filling direction. Second, it proposes directions to transfer its contained liquid to the outside while avoiding spillage. We compare our simplified model with a common fluid dynamics simulation and demonstrate that our algorithm makes human-like choices about the best directions to fill containers and transfer liquid from them. We apply our approach to reason about the containability of several real-world objects acquired using a consumer-grade depth camera.

Keywords: functionality, physics-based reasoning, affordance, vision for graphics, robotics

Publications:

Acknowledgements:

We thank Michael Brown for narrating the video and William Koh for scanning the data. Sai-Kit Yeung is supported by SUTD-ZJU Collaboration Research Grant 2012 SUTDZJU/RES/03/2012, SUTD-MIT International Design Center Grant IDG31300106, and Singapore MOE Academic Research Fund MOE2013-T2-1-159. Part of the work was done when Noah was visiting SUTD and supported by Singapore MOE Academic Research Fund MOE2013-T2-1-159. We acknowledge the support of the SUTD Digital Manufacturing and Design (DManD) Centre which is supported by the Singapore National Research Foundation. Lap-Fai Yu is supported by the University of Massachusetts Boston StartUp Grant P20150000029280 and the Joseph P. Healey Research Grant Program provided by the Office of the Vice Provost for Research and Strategic Initiatives & Dean of Graduate Studies of the University of Massachusetts Boston.

Results:

Containability analysis of some objects in our dataset:



Real-world examples. (a) An input scene showing a sealed can, a bowl and a cereal box on a table. Our algorithm suggests that the bowl is a container and all its transfer directions are equally-likely, and that the table after being inverted is also a possible container. (b) An input scene showing a cup, a plate, two speakers and a piece of cloth. The cup and plate are identified as possible containers. The cup is found to be more robust to slight perturbation, as it has a smaller rate of decrease of containee volume (V ) when slightly tilted. (c)-(g) Results on other real-world objects captured by a Structure Sensor. The hole-ridden tray in (g) is identified as a non-container.