Details

Autor(en) / Beteiligte

• Mo, Kaichun
• Guibas, Leonidas
• Mukadam, Mustafa
• Gupta, Abhinav
• Tulsiani, Shubham

Titel

Where2Act: From Pixels to Actions for Articulated 3D Objects

Ist Teil von

• 2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021, p.6793-6803

Ort / Verlag

IEEE

Erscheinungsjahr

2021

Quelle

IEEE/IET Electronic Library

Beschreibungen/Notizen

• One of the fundamental goals of visual perception is to allow agents to meaningfully interact with their environment. In this paper, we take a step towards that long-term goal - we extract highly localized actionable information related to elementary actions such as pushing or pulling for articulated objects with movable parts. For example, given a drawer, our network predicts that applying a pulling force on the handle opens the drawer. We propose, discuss, and evaluate novel network architectures that given image and depth data, predict the set of actions possible at each pixel, and the regions over articulated parts that are likely to move under the force. We propose a learning-from-interaction framework with an online data sampling strategy that allows us to train the network in simulation (SAPIEN) and generalizes across categories. Check the website for code and data release.