A Multi-scale CNN for Affordance Segmentation in RGB Images

@inproceedings{Roy2016AMC,
  title={A Multi-scale CNN for Affordance Segmentation in RGB Images},
  author={Anirban Roy and Sinisa Todorovic},
  booktitle={ECCV},
  year={2016}
}
Given a single RGB image our goal is to label every pixel with an affordance type. [] Key Method Our approach uses a deep architecture, consisting of a number of multi-scale convolutional neural networks, for extracting mid-level visual cues and combining them toward affordance segmentation. The mid-level cues include depth map, surface normals, and segmentation of four types of surfaces – namely, floor, structure, furniture and props. For evaluation, we augmented the NYUv2 dataset with new ground-truth…
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References

SHOWING 1-10 OF 49 REFERENCES
Learning Hierarchical Features for Scene Labeling
TLDR
A method that uses a multiscale convolutional network trained from raw pixels to extract dense feature vectors that encode regions of multiple sizes centered on each pixel, alleviates the need for engineered features, and produces a powerful representation that captures texture, shape, and contextual information.
Learning human activities and object affordances from RGB-D videos
TLDR
This work considers the problem of extracting a descriptive labeling of the sequence of sub-activities being performed by a human, and more importantly, of their interactions with the objects in the form of associated affordances, and formulate the learning problem using a structural support vector machine (SSVM) approach.
Perceptual Organization and Recognition of Indoor Scenes from RGB-D Images
TLDR
This work proposes algorithms for object boundary detection and hierarchical segmentation that generalize the gPb-ucm approach of [2] by making effective use of depth information and shows how this contextual information in turn improves object recognition.
Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-scale Convolutional Architecture
  • D. Eigen, R. Fergus
  • Computer Science
    2015 IEEE International Conference on Computer Vision (ICCV)
  • 2015
In this paper we address three different computer vision tasks using a single basic architecture: depth prediction, surface normal estimation, and semantic labeling. We use a multiscale convolutional
Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
TLDR
This paper employs two deep network stacks: one that makes a coarse global prediction based on the entire image, and another that refines this prediction locally, and applies a scale-invariant error to help measure depth relations rather than scale.
Recurrent Convolutional Neural Networks for Scene Parsing
TLDR
This work proposes an approach consisting of a recurrent convolutional neural network which allows us to consider a large input context, while limiting the capacity of the model, while remaining very fast at test time.
Affordance detection of tool parts from geometric features
TLDR
This work proposes two approaches for learning affordances from local shape and geometry primitives: superpixel based hierarchical matching pursuit (S-HMP); and structured random forests (SRF), and introduces a large RGB-Depth dataset where tool parts are labeled with multiple affordances and their relative rankings.
Recovering Surface Layout from an Image
TLDR
This paper takes the first step towards constructing the surface layout, a labeling of the image intogeometric classes, to learn appearance-based models of these geometric classes, which coarsely describe the 3D scene orientation of each image region.
Indoor Semantic Segmentation using depth information
TLDR
This work addresses multi-class segmentation of indoor scenes with RGB-D inputs by applying a multiscale convolutional network to learn features directly from the images and the depth information.
Indoor Segmentation and Support Inference from RGBD Images
TLDR
The goal is to parse typical, often messy, indoor scenes into floor, walls, supporting surfaces, and object regions, and to recover support relationships, to better understand how 3D cues can best inform a structured 3D interpretation.
...
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