Internet Vision - Lecture 3 Tamara Berg Sept 10

New Lecture Time Mondays 10:00am-12:30pm in 2311 Monday (9/15) we will have a general Computer Vision & Machine Learning review Please look at papers and decide which one you want to present by Monday – read topic/titles/abstracts to get an idea of which you are interested in

Thanks to Lalonde et al for providing slides!

Algorithm Outline

Inserting objects into images Have an image and want to add realistic looking objects to that image

Inserting objects into images User picks a location where they want to insert an object

Inserting objects into images Based some properties calculated about the image, possible objects are presented.

Inserting objects into images User selects which object to insert and the object is placed in the scene at the correct scale for the location

Inserting objects into images – Possible approaches Insert a clip art object Insert a clip art object with some idea of the environment Insert a rendered object with full model of the environment

Some objects will be easy to insert because they already “fit” into the scene

Collect a large database of objects. Let the computer decide which examples are easy to insert. Allow the user to select only among those.

When will an object “fit”? 1.) When the lighting conditions of the scene and object are similar 2.) When the camera pose of the scene & object match

2D vs 3D Use 3d information for: 1.) Annotating objects in the clip-art library with camera pose 2.) Estimating the camera pose in the query image 3.) Computing illumination context in both library & query images

Phase 1 - Database Annotation For each object we want: – Estimate of its true size and the camera pose it was captured under – Estimate of the lighting conditions it was captured under

Phase 1 - Database Annotation Estimate object size Objects closer to the camera appear larger than objects further from the camera

Phase 1 - Database Annotation Estimate object size *If* you know the camera pose then you can estimate the real height of an object from: location in the image, pixel height

Phase 1 - Database Annotation Estimate object size Annotate objects with their true heights and resize examples to a common reference size

Phase 1 - Database Annotation Estimate object size & camera pose Don’t know camera pose or object heights! Trick - Infer camera pose & object heights across all object classes in the database given only the height distribution for one class

Phase 1 - Database Annotation Estimate object size & camera pose Start with known heights for people

Phase 1 - Database Annotation Estimate object size & camera pose Estimate camera pose for images with multiple people

Phase 1 - Database Annotation Estimate object size & camera pose Use these images to estimate a prior over the distribution of poses How do people usually take pictures? Standing on the ground at eye level.

Phase 1 - Database Annotation Estimate object size & camera pose Use the learned pose distribution to estimate heights of other object categories that appear with people. Iteratively use these categories to learn more categories. Annotate all objects in the database with their true size and originating camera pose.

Phase 1 - Database Annotation Estimate object size & camera pose

Phase 1 - Database Annotation For each object we want: – Estimate of its true size and the camera pose it was captured under – Estimate of the lighting conditions it was captured under

Phase 1 - Database Annotation Estimate lighting conditions Estimate which pixels are ground, sky, vertical Black box for now (we’ll cover this paper later in the course) Ground Vertical Sky

Phase 1 - Database Annotation Estimate lighting conditions Distribution of pixel colors

Phase 2 – Object Insertion Query Image

Phase 2 – Object Insertion User specifies horizon line – use to calculate camera pose with respect to ground plane (lower -> tilted down, higher -> tilted up). Illumination context is calculated in the same way as for the database images.

Phase 2 – Object Insertion Insert an object into the scene that has matching lighting, and camera pose to the query image

Phase 2 – Object Insertion But wait it still looks funny!

Phase 2 – Object Insertion Shadows are important!

Phase 2 – Object Insertion

Shadow Transfer

Categorize images for easy selection in user interface

Big Picture It’s all about the data! Use lots of data to turn a hard problem into an easier one! – Place “my car” in a scene is much harder than place “some car” in a scene. Allow the computer to choose from among many examples of a class to find the easy ones.