Name
Abstract | ||
|---|---|---|
In this work we address the problem of indoor scene understanding from RGB-D images. Specifically, we propose to find instances of common furniture classes, their spatial extent, and their pose with respect to generalized class models. To accomplish this, we use a deep, wide, multi-output convolutional neural network (CNN) that predicts class, pose, and location of possible objects simultaneously. To overcome the lack of large annotated RGB-D training sets (especially those with pose), we use an on-the-fly rendering pipeline that generates realistic cluttered room scenes in parallel to training. We then perform transfer learning on the relatively small amount of publicly available annotated RGB-D data, and find that our model is able to successfully annotate even highly challenging real scenes. Importantly, our trained network is able to understand noisy and sparse observations of highly cluttered scenes with a remarkable degree of accuracy, inferring class and pose from a very limited set of cues. Additionally, our neural network is only moderately deep and computes class, pose and position in tandem, so the overall run-time is significantly faster than existing methods, estimating all output parameters simultaneously in parallel. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ICCV.2015.95 | ICCV |
Keywords | Field | DocType |
semantic pose,deep network,cluttered scene,sparse observation,noisy observation,real scene,transfer learning,realistic cluttered room scene,on-the-fly rendering pipeline,RGB-D training set,CNN,multioutput convolutional neural network,generalized class model,furniture class,RGB-D image,indoor scene understanding,synthetic RGB-D | Computer vision,Graphics pipeline,Pattern recognition,Computer science,Convolutional neural network,Transfer of learning,RGB color model,Artificial intelligence,Deep learning,Image-based modeling and rendering,Rendering (computer graphics),Artificial neural network | Journal |
Volume | Issue | ISSN |
abs/1508.00835 | 1 | 1550-5499 |
Citations | PageRank | References |
25 | 1.02 | 10 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jeremie Papon | 1 | 199 | 10.18 |
| Markus Schoeler | 2 | 145 | 5.98 |