Download - Indoor Scene Segmentation using a Structured Light Sensor

Indoor Scene Segmentation using a Structured Light Sensor

Nathan Silberman and Rob Fergus

ICCV 2011 Workshop on 3D Representation and Recognition

Courant Institute

Overview

Indoor Scene Recognition using the Kinect• Introduce new Indoor Scene Depth Dataset• Describe CRF-based model– Explore the use of rgb/depth cues

Motivation• Indoor Scene recognition is hard– Far less texture than outdoor scenes– More geometric structure

Motivation• Indoor Scene recognition is hard– Far less texture than outdoor scenes– More geometric structure

• Kinect gives us depth map (and RGB)– Direct access to shape and geometry information

Overview


Capturing our Dataset

Statistics of the DatasetScene Type Number of

Scenes Frames Labeled Frames *

Bathroom 6 5,588 76

Bedroom 17 22,764 480

Bookstore 3 27,173 784

Cafe 1 1,933 48

Kitchen 10 12,643 285

Living Room 13 19,262 355

Office 14 19,254 319

Total 64 108,617 2,347

* Labels obtained via LabelMe

Dataset Examples

Living Room

RGB Raw Depth Labels

Dataset Examples

Living Room

RGB Depth* Labels

* Bilateral Filtering used to clean up raw depth image

Dataset Examples

Bathroom

RGB Depth Labels

Dataset Examples

Bedroom

RGB Depth Labels

Existing Depth Datasets

[1] K. Lai, L. Bo, X. Ren, and D. Fox. A Large-Scale Hierarchical Multi-View RGB-D Object Dataset. ICRA 2011 [2] B. Liu, S. Gould and D. Koller. Single Image Depth Estimation from Predicted Semantic Labels. CVPR 2010

RGB-D Dataset [1]

Stanford Make3d [2]

Existing Depth Datasets

[1] Abhishek Anand, Hema Swetha Koppula, Thorsten Joachims, Ashutosh Saxena. Semantic Labeling of 3D Point Clouds for Indoor Scenes. NIPS, 2011[2] A. Janoch, S. Karayev, Y. Jia, J. T. Barron, M. Fritz, K. Saenko, T. Darrell. A Category-Level 3-D Object Dataset: Putting the Kinect to Work. ICCV Workshop on Consumer Depth Cameras for Computer Vision. 2011

Point Cloud Data [1] B3DO [2]

Dataset Freely Availablehttp://cs.nyu.edu/~silberman/nyu_indoor_scenes.html

Overview


Segmentation using CRF ModelCost(labels) = Local Terms(label i) +

Spatial Smoothness (label i, label j)€

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• Standard CRF formulation• Optimized via graph cuts• Discrete label set (~12 classes)

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i∈ pixels

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i, j ∈ pairs of pixels

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ModelCost(labels) = Local Terms(label i) +

Spatial Smoothness (label i, label j)

= Appearance(label i | descriptor i) Location(i)€

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i∈ pixels

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i, j∈ pairs of pixels

Appearance Term

Appearance(label i | descriptor i)

Several Descriptor Types to choose from:o RGB-SIFTo Depth-SIFTo Depth-SPINo RGBD-SIFTo RGB-SIFT/D-SPIN

Descriptor Type: RGB-SIFT

Extracted Over Discrete Grid

RGB image from the Kinect

128 D

Descriptor Type: Depth-SIFTDepth image from kinect with linear scaling

128 D


Descriptor Type: Depth-SPINDepth image from kinect with linear scaling

50 D

Radius

Depth

A. E. Johnson and M. Hebert. Using spin images for efficient object recognition in cluttered 3d scenes. IEEE PAMI, 21(5):433–449, 1999


Descriptor Type: RGBD-SIFT

Concatenate

256 D


Depth image from kinectwith linear scaling

Descriptor Type: RGD-SIFT, D-SPIN

Concatenate


Depth image from kinectwith linear scaling

178 D

Appearance Model

Descriptor at each location

Appearance(label i | descriptor i)- Modeled by a Neural Network with a

single hidden layer

Appearance Model



13 Classes

1000-D Hidden Layer

128/178/256-D Input

Softmax output layer

Appearance Model

13 Classes

1000-D Hidden Layer

128/178/256-D Input


Probability Distribution over classes


Interpreted as p(label | descriptor)

Appearance Model

13 Classes

1000-D Hidden Layer

128/178/256-D Input


Probability Distribution over classes


Trained with backpropagation




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3D Priors2D Priors

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Location Priors: 2D

• 2D Priors are histograms of P(class, location)• Smoothed to avoid image-specific artifacts

Motivation: 3D Location Priors

• 2D Priors don’t capture 3d geomety• 3D Priors can be built from depth data

• Rooms are of different shapes and sizes, how do we align them?

Motivation: 3D Location Priors

• To align rooms, we’ll use a normalized cylindrical coordinate system:

Band of maximum depths along each vertical scanline

Relative Depth DistributionsTable Television

Bed Wall

Relative Depth

Density

0 01 1

Location Priors: 3D




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3D Priors2D Priors

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Penalty for adjacent labels disagreeing(Standard Potts Model)



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Spatial Modulation of Smoothness• None• RGB Edge • Depth Edges• RGB + Depth Edges

• Superpixel Edges• Superpixel + RGB Edges• Superpixel + Depth Edges

Experimental Setup

• 60% Train (~1408 images)• 40% Test (~939 images)• 10 fold cross validation• Images of the same scene cannot appear apart• Performance criteria is pixel-level classification

(mean diagonal of confusion matrix)• 12 most common classes, 1 background class

(from the rest)

Evaluating Descriptors

2D Descriptors 3D Descriptors

Perc

ent

RGB-SIFT Depth-SIFT Depth-SPIN RGBD-SIFT RGB-SIFT/D-SPIN30

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UnaryCRF

Evaluating Location Priors

RGB-SIFT

RGB-SIFT

+2D Prio

rs

RGBD-SIFT

RGBD-SIFT

+2D Prio

rs

RGBD-SIFT

+3D Prio

rs

RGBD-SIFT

+3D Prio

rs (ab

s)30

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UnaryCRF

Perc

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2D Descriptors 3D Descriptors

Conclusion

• Kinect Depth signal helps scene parsing• Still a long way from great performance• Shown standard approaches on RGB-D data.• Lots of potential for more sophisticated

methods.• No complicated geometric reasoning• http://cs.nyu.edu/~silberman/nyu_indoor_scenes.html

Preprocessing the Data

[1] N. Burrus. Kinect RGB Demo v0.4.0. http://nicolas.burrus.name/index.php/Research/KinectRgbDemoV4?from=Research.KinectRgbDemoV2, Feb. 2011

We use open source calibration software [1] to infer:• Parameters of RGB & Depth cameras• Homography between cameras.

Preprocessing the data

• Bilateral filter used to diffuse depth across regions of similar RGB intensity

• Naïve GPU implementation runs in ~100 ms

Motivation

Results from Spatial Pyramid-based classification [1] using 5 indoor scene types. Contrast this with the 81% received by [1] on a 13-class (mostly outdoor) scene dataset. They note similar confusion within indoor scenes.[1] Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene CategoriesS. Lazebnik, C. Schmid, and J. Ponce, CVPR 2006