“SemanticPaint: Interactive Segmentation and Learning of 3D World” by Valentin, Vineet, Cheng, Kim, Shotton, et al. …
Conference:
Type:
Entry Number: 77
Title:
- SemanticPaint: Interactive Segmentation and Learning of 3D World
Session/Category Title: SHAPE SIGNATURE
Presenter(s)/Author(s):
- Julien P. C. Valentin
- Vibhav Vineet
- Ming-Ming Cheng
- David Kim
- Jamie Shotton
- Pushmeet Kohli
- Matthias Nießner
- Antonio Criminisi
- Shahram Izadi
- Philip H. S. Torr
Moderator(s):
Abstract:
We present a real-time, interactive system for the geometric reconstruction, object-class segmentation and learning of 3D scenes [Valentin et al. ]. Using our system, a user can walk into a room wearing a consumer depth camera and a virtual reality headset, and both densely reconstruct the 3D scene [Nießner et al. 2013]) and interactively segment the environment into object classes such as ‘chair’, ‘floor’ and ‘table’. The user interacts physically with the real-world scene, touching or pointing at objects and using voice commands to assign them appropriate labels. These user generated labels are leveraged by a new online random forest-based machine learning algorithm, which is used to predict labels for previously unseen parts of the scene. The predicted labels, together with those provided directly by the user, are incorporated into a dense 3D conditional random field model, over which we perform mean-field inference to filter out label inconsistencies. The entire pipeline runs in real time, and the user stays ‘in the loop’ throughout the process, receiving immediate feedback about the progress of the labelling and interacting with the scene as necessary to refine the predicted segmentation.
References:
Niessner, M., Zollhöfer, M., Izadi, S., and Stamminger, M. 2013. Real-time 3d reconstruction at scale using voxel hashing. ACM Transactions on Graphics 32, 6, 169.
Valentin, J., Vineet, V., Cheng, M.-M., Kim, D., Shotton, J., Kohli, P., Niessner, M., Criminisi, A., Izadi, S., and Torr, P. Semanticpaint: Interactive 3d labeling and learning at your fingertips. To appear in ACM Transactions on Graphics.
