Estimating depth from a single image

Patent metadata

Field	Value
Publication number	US-2016124995-A1
Application number	US-201614994459-A
Country	US
Kind code	A1
Filing date	Jan 13, 2016
Priority date	Sep 5, 2013
Publication date	May 5, 2016
Grant date	—

Abstract

During a training phase, a machine accesses reference images with corresponding depth information. The machine calculates visual descriptors and corresponding depth descriptors from this information. The machine then generates a mapping that correlates these visual descriptors with their corresponding depth descriptors. After the training phase, the machine may perform depth estimation based on a single query image devoid of depth information. The machine may calculate one or more visual descriptors from the single query image and obtain a corresponding depth descriptor for each visual descriptor from the generated mapping. Based on obtained depth descriptors, the machine creates depth information that corresponds to the submitted single query image.

First claim

What is claimed is:

1 . A method comprising:
accessing reference images and corresponding reference depth maps from a reference database, a first reference image corresponding to a first reference depth map and including a color pixel defined by at least three color values, the first reference depth map including a depth value that corresponds to the color pixel in the first reference image; calculating visual descriptors and corresponding depth descriptors from the accessed reference images and their corresponding reference depth maps; generating a matrix that correlates the calculated visual descriptors with their calculated corresponding depth descriptors, the generating of the matrix being performed by a processor of a machine; receiving a query image; calculating a visual descriptor from the received query image; obtaining a depth descriptor that corresponds to the calculated visual descriptor from the generated matrix; and creating a depth map that corresponds to the query image based on the obtained depth descriptor that corresponds to the visual descriptor calculated from the query image.

2 . The method of claim 1 , wherein:
the receiving of the query image receives the query image without any corresponding depth map.

3 . The method of claim 1 , wherein:
the reference images and the query image are red-green-blue (RGB) images that contain only RGB values and are devoid of depth values.

4 . The method of claim 1 , wherein:
the reference images are reference RGB images; and the accessing of the reference images and corresponding depth maps includes accessing reference red-green-blue-depth (RGB-D) images from the reference database, each reference RGB-D image including one of the reference RGB images and its corresponding reference depth map.

5 . The method of claim 1 , wherein:
the receiving of the query image receives the query image as part of a request to estimate depth information solely from the query image; and the creating of the depth map that corresponds to the query image is in response to the request to estimate the depth information.

6 . The method of claim 1 further comprising:
partitioning the query image into superpixels; and
modifying the created depth map that corresponds to the query image based on the superpixels in the query image.

7 . The method of claim 6 , wherein:
the modifying of the created depth map includes assigning a constant depth value to each pixel within a superpixel in the query image.

8 . The method of claim 6 , wherein:
the modifying of the created depth map includes modifying an orientation of a plane represented by a superpixel in the query image in accordance with a random sample consensus (RANSAC) algorithm.

9 . The method of claim 1 , wherein:
the first reference depth map that corresponds to the first reference image is a first reference depth image that includes a depth pixel that is defined by the depth value and corresponds to the color pixel in the first reference image.

10 . The method of claim 1 , wherein:
the query image depicts a surface of a physical object and includes camera information; the created depth map includes a three-dimensional representation of the surface of the physical object whose surface is depicted in the query image; and the method further comprises generating a three-dimensional model of the surface of the physical object based on the camera information included in the query image and based on the created depth map that corresponds to the query image that depicts the physical object.

11 . The method of claim 10 further comprising:
providing the generated three-dimensional model to a three-dimensional rendering engine to create a three-dimensional visualization of the surface of the physical object.

12 . The method of claim 10 , wherein:
the generated three-dimensional model is a three-dimensional cloud of points among which are points that represent the surface of the physical object; and the method further comprises calculating a length of the surface of the physical object based on the generated three-dimensional cloud of points.

13 . The method of claim 12 , wherein:
the physical object depicted in the query image is a shippable item; and the method further comprises providing the calculated length of the surface of the shippable item to a shipping application.

14 . A system comprising:
one or more processors; a database trainer module that configures at least one processor among the one or more processors to:
access reference images and corresponding reference depth maps from a reference database, a first reference image corresponding to a first reference depth map and including a color pixel defined by at least three color values, the first reference depth map including a depth value that corresponds to the color pixel in the first reference image;
calculate visual descriptors and corresponding depth descriptors from the accessed reference images and their corresponding reference depth maps; and
generate a matrix that correlates the calculated visual descriptors with their calculated corresponding depth descriptors; and
a depth map module that configures at least one processor among the one or more processors to:
receive a query image;
calculate a visual descriptor from the received query image;
obtain a depth descriptor that corresponds to the calculated visual descriptor from the matrix generated by the trainer module; and
create a depth map that corresponds to the query image based on the obtained depth descriptor that corresponds to the visual descriptor calculated from the query image.

15 . The system of claim 14 , wherein the depth map module further configures the at least one processor to:
receive the query image as part of a request to estimate depth information solely from the query image; and create the depth map that corresponds to the query image in response to the request to estimate the depth information.

16 . The system of claim 14 , wherein the depth map module further configures the at least one processor to:
partition the query image into superpixels; and modify the created depth map that corresponds to the query image based on the superpixels in the query image.

17 . The system of claim 14 , wherein:
the query image depicts a surface of a physical object and includes camera information; the created depth map includes a three-dimensional representation of the surface of the physical object whose surface is depicted in the query image; and the system further comprises a visualization module configured to generate a three-dimensional model of the surface of the physical object based on the camera information included in the query image and based on the created depth map that corresponds to the query image that depicts the physical object.

18 . The system of claim 17 , wherein:
the physical object depicted in the query image is a shippable item; the generated three-dimensional model is a three-dimensional cloud of points among which are points that represent the surface of the shippable item; and the system further comprises a shipping module configured to:
calculate a length of the surface of the shippable item based on the generated three-dimensional cloud of points; and
provide the calculated length of the surface of the shippable item to a shipping application.

19 . A non-transitory machine-readable storage medium comprising instructions that, when executed by one or more processors of a machine, cause the mac

Assignees

• Ebay Inc

Inventors

• Bhardwaj Anurag
• Baig Mohammad Haris
• Piramuthu Robinson
• Jagadeesh Vignesh
• Di Wei

Classifications

• G06F17/30256 Primary

  Physics · mapped topic

• G06T7/0051

  Physics · mapped topic

• G06F17/3025

  Physics · mapped topic

• G06F17/30262

  Physics · mapped topic

• G06T2207/20081

  Training; Learning · CPC title