3D SCENE MODEL FROM VIDEO

1 . A method for determining a three-dimensional model of a scene from a digital video captured using a digital video camera, the digital video including a temporal sequence of video frames, each video frame having an array of image pixels, comprising:

determining a camera position of the digital video camera for each video frame;

determining a smoothed camera path responsive to the camera positions;

determining a sequence of target camera positions spaced out along the smoothed camera path such that video frames captured at the target camera positions have at least a target level of overlapping scene content;

selecting a sequence of target video frames from the temporal sequence of video frames based on the target camera positions; and

analyzing the target video frames using a three-dimensional reconstruction process to determine a three-dimensional model of the scene;

wherein the method is implemented at least in part by a data processing system.

2 . The method of claim 1 wherein the sequence of target video positions are determined by:

determining a distance interval such that a pair of video frames captured at camera positions separated by the distance interval have an amount of overlapping scene content in accordance with the target level of overlapping scene content;

determining the sequence of target camera positions by sampling the smoothed camera path based on the distance interval.

3 . The method of claim 1 wherein the sequence of target video positions are sequentially determined such that each succeeding target camera position is spaced out as far apart as possible along the smoothed camera path from the previous target camera position while satisfying the condition that video frames captured at the camera positions closest to the target camera positions have at least the target level of overlapping scene content.

4 . The method of claim 1 wherein the level of overlapping scene content in two video frames is characterized by a number of matching features for the two video frames, and wherein the target level of overlapping scene content is defined by a target number of matching features.

5 . The method of claim 1 wherein the level of overlapping scene content in two video frames is characterized by a size of an overlap area between the two video frames, and wherein the target level of overlapping scene content is defined by a target overlap area size.

6 . The method of claim 1 wherein the camera positions for the video frames are determined by analyzing the images pixels of the video frames.

7 . The method of claim 6 wherein the camera positions are determined using a structure-from-motion algorithm.

8 . The method of claim 1 wherein the camera positions are determined using a position sensor in the digital video camera.

9 . The method of claim 1 wherein the smoothed camera path is determined by fitting a spline function to the set of determined camera positions.

10 . The method of claim 1 wherein selected target video frames are the video frames having associated camera positions which are closest to the target camera positions.

11 . The method of claim 1 wherein the three-dimensional reconstruction process is a multi-view-stereo reconstruction process.

12 . The method of claim 1 wherein the three-dimensional model is a three-dimensional point cloud model or a three-dimensional mesh model.

13 . The method of claim 1 further including:

analyzing the camera positions to identify image frames having redundant camera positions; and

discarding at least some of the identified video frames having the redundant camera positions.

14 . The method of claim 13 wherein two camera positions are designated to be redundant if they are less than a predetermined distance away from each other.

15 . The method of claim 1 further including:

analyzing the video frames to determine corresponding image quality metric values, and

discarding video frames having image quality metric values that are less than a predefined threshold.

16 . The method of claim 15 wherein the image quality metric values are determined based on estimating image sharpness, image blur or image noise.