An electronic apparatus and method for sequence stabilization of point cloud frames using motion information is disclosed. The electronic apparatus receives image data that includes images of objects. The image data corresponds to a duration in which in the objects are in a dynamic state. Based on the image data, the electronic apparatus generates a point cloud sequence and extracts motion information associated with the objects. The electronic apparatus determines a first set of 3D points of a first point cloud frame that is in a static state with respect to a second set of 3D points of a second point cloud frame, based on the motion information. The first and second point cloud frames are consecutive frames of point cloud sequence. The electronic apparatus further determines a difference between the first and the second set of 3D points and updates the first point cloud frame based on the difference.

Systems and methods for automated detection of changes in extent of structures using imagery are disclosed, including a non-transitory computer readable medium storing computer executable code that when executed by a processor cause the processor to: align, with an image classifier model, an outline of a structure at a first instance of time to pixels within an image depicting the structure captured at a second instance of time; assess a degree of alignment between the outline and the pixels depicting the structure, so as to classify similarities between the structure depicted within the pixels of the image and the outline using a machine learning model to generate an alignment confidence score; and determine an existence of a change in the structure based upon the alignment confidence score indicating a level of confidence below a predetermined threshold level of confidence that the outline and the pixels within the image are aligned.

Aspects include a system and method for detection of computer-aided design (CAD) objects in point clouds. An example method includes obtaining, by a processing device, a labeled data set. The method further includes training, by the processing device, a model on the labeled data set using a two-dimensional (2D) object detector to calculate a three-dimensional (3D) box out of a detected 2D box by mapping coordinates on a geometric primitive image into 3D. The method further includes fitting, by the processing device, a computer-aided design (CAD) model into the 3D box.

A method performed by a computing system comprises receiving a fluoroscopic image of a patient anatomy while a portion of a medical instrument is positioned within the patient anatomy. The fluoroscopic image has a fluoroscopic frame of reference. The portion has a sensed position in an anatomic model frame of reference. The method further comprises identifying the portion in the fluoroscopic image and identifying an extracted position of the portion in the fluoroscopic frame of reference using the identified portion in the fluoroscopic image. The method further comprises registering the fluoroscopic frame of reference to the anatomic model frame of reference based on the sensed position of the portion and the extracted position of the portion.

Described herein are systems, methods, and non-transitory computer readable media for performing an alignment between a first vehicle sensor and a second vehicle sensor. Two-dimensional (2D) data indicative of a scene within an environment being traversed by a vehicle is captured by the first vehicle sensor such as a camera or a collection of multiple cameras within a sensor assembly. A three-dimensional (3D) representation of the scene is constructed using the 2D data. 3D point cloud data also indicative of the scene is captured by the second vehicle sensor, which may be a LiDAR. A 3D point cloud representation of the scene is constructed based on the 3D point cloud data. A rigid transformation is determined between the 3D representation of the scene and the 3D point cloud representation of the scene and the alignment between the sensors is performed based at least in part on the determined rigid transformation.

The present disclosure relates to methods and systems for vascular image processing. The method may include obtaining an initial vascular image, generating a vascular fragment image by performing a vascular fragmentation operation on the initial vascular image, and generating, based on the vascular fragment image, a vascular centerline image.

Methods and apparatus related to generating a model for an object encountered by a robot in its environment, where the object is one that the robot is unable to recognize utilizing existing models associated with the robot. The model is generated based on vision sensor data that captures the object from multiple vantages and that is captured by a vision sensor associated with the robot, such as a vision sensor coupled to the robot. The model may be provided for use by the robot in detecting the object and/or for use in estimating the pose of the object.

The present disclosure provides a surgical robot including a control system, a force identification system, a robotic arm system and a navigation system, the robotic arm system including a robotic arm, a robotic arm terminal detachably connected to a trackable element. The navigation system acquires and provides a registration point of interest on an object to the robotic arm system. The robotic arm system controls movements of the robotic arm to drive the trackable element to move to the registration point of interest. The force identification system detects and provides a force applied to the robotic arm terminal to the control system. The control system determines whether the trackable element has moved to the registration point of interest on the object. The present disclosure also provides a method for bone registration of the surgical robot.

Systems and methods for generating a three-dimensional (3D) model of a user's dental arch based on two-dimensional (2D) images of dental impressions include a model training system that provides a machine learning model using training image(s) of a dental impression of a respective dental arch and a 3D training model of the respective dental arch. A model generation system receives first image(s) of a first dental impression of a user's dental arch and second image(s), which may be of the first dental impression or a second dental impression of the dental arch. The model generation system generates a first and second 3D model of the dental arch by applying the first image(s) and second image(s) to the machine learning model.

A surgical guidance system has two cameras to provide stereo image stream of a surgical field; and a stereo viewer. The system has a 3D surface extraction module that generates a first 3D model of the surgical field from the stereo image streams; a registration module for co-registering annotating data with the first 3D model; and a stereo image enhancer for graphically overlaying at least part of the annotating data onto the stereo image stream to form an enhanced stereo image stream for display, where the enhanced stereo stream enhances a surgeon's perception of the surgical field. The registration module has an alignment refiner to adjust registration of the annotating data with the 3D model based upon matching of features within the 3D model and features within the annotating data; and in an embodiment, a deformation modeler to deform the annotating data based upon a determined tissue deformation.