FAST, DYNAMIC REGISTRATION WITH AUGMENTED REALITY
Fast, dynamic registration with augmented reality includes registering a model point cloud to a point cloud of an object, including obtaining selection of an origin point for the model point cloud as a sampled surface point on the object in an established collection of sample points of the object, the collection forming the point cloud of the object; obtaining other sampled surface point(s) on the object and including those in the collection; determining an initial pose of the model point cloud based on the collection of sample points; obtaining an additional sampled surface point and updating the collection to include such; determining a fit of the model point cloud to the point cloud of the object based on the updated collection; determining a registration accuracy of the fit of the model point cloud to the point cloud of the object; and performing processing based on the determined registration accuracy.
1 . A computer implemented method comprising:
registering a model point cloud to a point cloud of an object, the registering comprising:
obtaining a user selection of an origin point for the model point cloud, the origin point being a sampled surface point on the object and being a first point included in an established collection of sample points of the object, the collection forming the point cloud of the object;
obtaining one or more other sampled surface points on the object and including the obtained one or more other sampled surface points in the collection;
determining an initial pose of the model point cloud based on the collection of sample points of the object;
obtaining an additional sampled surface point on the object and updating the collection of sample points to include the additional sampled surface point and thereby provide an updated collection of sample points;
determining a fit of the model point cloud to the point cloud of the object based on the updated collection of sample points of the object;
determining a registration accuracy of the fit of the model point cloud to the point cloud of the object; and
performing processing based on the determined registration accuracy.
2 . The method of claim 1 , wherein the model point cloud comprises an anatomy model point cloud and wherein the object comprises a patient anatomy.
3 . The method of claim 2 , wherein the performing processing comprises, based on the determined registration accuracy being less than a preconfigured threshold level of accuracy: iterating, one or more times, the obtaining an additional sampled surface point, the determining a fit, and the determining the registration accuracy.
4 . The method of claim 3 , wherein the iterating halts based on the determined registration accuracy being at least the preconfigured threshold level of accuracy.
5 . The method of claim 4 , wherein based on halting the iterating, the determined fit of the anatomy model point cloud to the point cloud of the patient anatomy provides a registration of the anatomy model point cloud to the point cloud of the patient anatomy, and wherein the method further comprises determining and digitally presenting to a surgeon one or more indications of surgical guidance.
6 . The method of claim 2 , wherein obtaining the user selection of the origin point comprises providing an anatomy model augmented reality (AR) element overlaying a portion of a view to the patient anatomy, the view showing a registration probe, and the anatomy model AR element being provided at a fixed position relative to a probe tip of the probe, wherein user movement of the probe repositions the anatomy model AR element and wherein the user selection comprises: the user positioning and orienting the anatomy model AR element in the view to overlay the patient anatomy by touching the patient anatomy with the probe tip, and providing input to select the origin point as a position of the probe tip touching the patient anatomy.
7 . The method of claim 6 , wherein obtaining the user selection of the origin point further comprises providing a probe axis AR element overlaying another portion of the view to the patient anatomy, the probe axis AR element comprising an axis line extending from the probe at a first position and away from the probe tip to a second position, the axis line representing an axis of the probe.
8 . The method of claim 2 , wherein determining the fit of the anatomy model point cloud to the point cloud of the patient anatomy based on the updated collection of sample points of the patient anatomy comprises:
performing a rough fitting of the anatomy model point cloud to the point cloud of the patient anatomy using the updated collection of sample points of the patient anatomy; and
based on performing the rough fitting, performing a fine fitting of the anatomy model point cloud to the point cloud of the patient anatomy using the updated collection of sample points of the patient anatomy.
9 . The method of claim 8 , wherein performing the rough fitting comprises applying a random sample consensus (RANSAC) algorithm and/or performing the fine fitting comprises applying an iterative closest point (ICP) algorithm.
10 . The method of claim 2 , wherein determining the initial pose of the anatomy model point cloud comprises performing a rough fitting of the anatomy model point cloud to the point cloud of the patient anatomy by applying a random sample consensus (RANSAC) algorithm and, based on performing the rough fitting, performing a fine fitting of the anatomy model point cloud to the point cloud of the patient anatomy by applying an iterative closest point (ICP) algorithm.
11 . A computer system comprising:
a memory; and
a processor in communication with the memory, wherein the computer system is configured to perform a method comprising:
registering a model point cloud to a point cloud of an object, the registering comprising:
obtaining a user selection of an origin point for the model point cloud, the origin point being a sampled surface point on the object and being a first point included in an established collection of sample points of the object, the collection forming the point cloud of the object;
obtaining one or more other sampled surface points on the object and including the obtained one or more other sampled surface points in the collection;
determining an initial pose of the model point cloud based on the collection of sample points of the object;
obtaining an additional sampled surface point on the object and updating the collection of sample points to include the additional sampled surface point and thereby provide an updated collection of sample points;
determining a fit of the model point cloud to the point cloud of the object based on the updated collection of sample points of the object;
determining a registration accuracy of the fit of the model point cloud to the point cloud of the object; and
performing processing based on the determined registration accuracy.
12 . The computer system of claim 11 , wherein the model point cloud comprises an anatomy model point cloud and wherein the object comprises a patient anatomy.
13 . The computer system of claim 12 , wherein the performing processing comprises, based on the determined registration accuracy being less than a preconfigured threshold level of accuracy: iterating, one or more times, the obtaining an additional sampled surface point, the determining a fit, and the determining the registration accuracy.
14 . The computer system of claim 13 , wherein the iterating halts based on the determined registration accuracy being at least the preconfigured threshold level of accuracy.
15 . The computer system of claim 14 , wherein based on halting the iterating, the determined fit of the anatomy model point cloud to the point cloud of the patient anatomy provides a registration of the anatomy model point cloud to the point cloud of the patient anatomy, and wherein the method further comprises determining and digitally presenting to a surgeon one or more indications of surgical guidance.
16 . The computer system of claim 12 , wherein obtaining the user selection of the origin point comprises providing an anatomy model augmented reality (AR) element overlaying a portion of a view to the patient anatomy, the view showing a registration probe, and the anatomy model AR element being provided at a fixed position relative to a probe tip of the probe, wherein user movement of the probe repositions the anatomy model AR element and wherein the user selection comprises: the user positioning and orienting the anatomy model AR element in the view to overlay the patient anatomy by touching the patient anatomy with the probe tip, and providing input to select the origin point as a position of the probe tip touching the patient anatomy.
17 . The computer system of claim 16 , wherein obtaining the user selection of the origin point further comprises providing a probe axis AR element overlaying another portion of the view to the patient anatomy, the probe axis AR element comprising an axis line extending from the probe at a first position and away from the probe tip to a second position, the axis line representing an axis of the probe.
18 . The computer system of claim 12 , wherein determining the fit of the anatomy model point cloud to the point cloud of the patient anatomy based on the updated collection of sample points of the patient anatomy comprises:
performing a rough fitting of the anatomy model point cloud to the point cloud of the patient anatomy using the updated collection of sample points of the patient anatomy; and
based on performing the rough fitting, performing a fine fitting of the anatomy model point cloud to the point cloud of the patient anatomy using the updated collection of sample points of the patient anatomy.
19 . The computer system of claim 18 , wherein performing the rough fitting comprises applying a random sample consensus (RANSAC) algorithm and/or performing the fine fitting comprises applying an iterative closest point (ICP) algorithm.
20 . The computer system of claim 12 , wherein determining the initial pose of the anatomy model point cloud comprises performing a rough fitting of the anatomy model point cloud to the point cloud of the patient anatomy by applying a random sample consensus (RANSAC) algorithm and, based on performing the rough fitting, performing a fine fitting of the anatomy model point cloud to the point cloud of the patient anatomy by applying an iterative closest point (ICP) algorithm.
21 . A computer program product comprising:
a computer readable storage medium readable by a processing circuit and storing instructions for execution by the processing circuit for performing a method comprising:
registering a model point cloud to a point cloud of an object, the registering comprising:
obtaining a user selection of an origin point for the model point cloud, the origin point being a sampled surface point on the object and being a first point included in an established collection of sample points of the object, the collection forming the point cloud of the object;
obtaining one or more other sampled surface points on the object and including the obtained one or more other sampled surface points in the collection;
determining an initial pose of the model point cloud based on the collection of sample points of the object;
obtaining an additional sampled surface point on the object and updating the collection of sample points to include the additional sampled surface point and thereby provide an updated collection of sample points;
determining a fit of the model point cloud to the point cloud of the object based on the updated collection of sample points of the object;
determining a registration accuracy of the fit of the model point cloud to the point cloud of the object; and
performing processing based on the determined registration accuracy.
22 . The computer program product of claim 21 , wherein the model point cloud comprises an anatomy model point cloud and wherein the object comprises a patient anatomy.
23 . The computer program product of claim 22 , wherein the performing processing comprises, based on the determined registration accuracy being less than a preconfigured threshold level of accuracy: iterating, one or more times, the obtaining an additional sampled surface point, the determining a fit, and the determining the registration accuracy.
24 . The computer program product of claim 23 , wherein the iterating halts based on the determined registration accuracy being at least the preconfigured threshold level of accuracy.
25 . The computer program product of claim 24 , wherein based on halting the iterating, the determined fit of the anatomy model point cloud to the point cloud of the patient anatomy provides a registration of the anatomy model point cloud to the point cloud of the patient anatomy, and wherein the method further comprises determining and digitally presenting to a surgeon one or more indications of surgical guidance.
26 . The computer program product of claim 22 , wherein obtaining the user selection of the origin point comprises providing an anatomy model augmented reality (AR) element overlaying a portion of a view to the patient anatomy, the view showing a registration probe, and the anatomy model AR element being provided at a fixed position relative to a probe tip of the probe, wherein user movement of the probe repositions the anatomy model AR element and wherein the user selection comprises: the user positioning and orienting the anatomy model AR element in the view to overlay the patient anatomy by touching the patient anatomy with the probe tip, and providing input to select the origin point as a position of the probe tip touching the patient anatomy.
27 . The computer program product of claim 26 , wherein obtaining the user selection of the origin point further comprises providing a probe axis AR element overlaying another portion of the view to the patient anatomy, the probe axis AR element comprising an axis line extending from the probe at a first position and away from the probe tip to a second position, the axis line representing an axis of the probe.
28 . The computer program product of claim 22 , wherein determining the fit of the anatomy model point cloud to the point cloud of the patient anatomy based on the updated collection of sample points of the patient anatomy comprises:
performing a rough fitting of the anatomy model point cloud to the point cloud of the patient anatomy using the updated collection of sample points of the patient anatomy; and
based on performing the rough fitting, performing a fine fitting of the anatomy model point cloud to the point cloud of the patient anatomy using the updated collection of sample points of the patient anatomy.
29 . The computer program product of claim 28 , wherein performing the rough fitting comprises applying a random sample consensus (RANSAC) algorithm and/or performing the fine fitting comprises applying an iterative closest point (ICP) algorithm.
30 . The computer program product of claim 22 , wherein determining the initial pose of the anatomy model point cloud comprises performing a rough fitting of the anatomy model point cloud to the point cloud of the patient anatomy by applying a random sample consensus (RANSAC) algorithm and, based on performing the rough fitting, performing a fine fitting of the anatomy model point cloud to the point cloud of the patient anatomy by applying an iterative closest point (ICP) algorithm.