A shooting method executed by a shooting device includes: shooting first images of a target space; generating a first three-dimensional point cloud of the target space, based on the first images and a first shooting position and a first shooting orientation of each of the first images; and determining a first region of the target space for which generating a second three-dimensional point cloud which is denser than the first three-dimensional point cloud is difficult, using the first three-dimensional point cloud and without generating the second three-dimensional point cloud. The determining includes generating a mesh using the first three-dimensional point cloud, and determining the region other than a second region of the target space for which the mesh is generated.

A method for controlling a distance measurement apparatus including a light emitting device capable of changing a direction of emission of a light beam and a light receiving device that detects a reflected light beam includes acquiring data representing a plurality of images acquired at different points in time by an image sensor that acquires an image of a scene, determining, on the basis of the data representing the plurality of images, a degree of priority of distance measurement of one or more physical objects included in the plurality of images, and executing distance measurement of the one or more physical objects by causing the light emitting device to emit the light beam in a direction corresponding to the degree of priority and in an order corresponding to the degree of priority and causing the light receiving device to detect the reflected light beam.

A camera system. In some embodiments, the camera system includes a first laser, a camera, and a processing circuit connected to the first laser and to the camera. The first laser may be steerable, and the camera may include a pixel including a photodetector and a pixel circuit, the pixel circuit including a first time-measuring circuit.

A lane extraction method uses projection transformation of a 3D point cloud map, by which the amount of operations required to extract the coordinates of a lane is reduced by performing deep learning and lane extraction in a two-dimensional (2D) domain, and therefore, lane information is obtained in real time. In addition, black-and-white brightness, which is most important information for lane extraction on an image, is substituted by the reflection intensity of a light detection and ranging (LiDAR) sensor so that a deep learning model capable of accurately extracting a lane is provided. Therefore, reliability and competitiveness is enhanced in the field of autonomous driving, the field of road recognition, the field of lane recognition, and the field of HD road maps for autonomous driving, and the fields similar or related thereto, and more particularly, in the fields of road recognition and autonomous driving using LiDAR.

A method for colourising a three-dimensional point cloud including surveying a point cloud with a surveying instrument. Each point of the point cloud may be characterised by coordinates within an instrument coordinate system having an instrument center. The method may include capturing a first image of the setting with a first camera. Each pixel value of the first image is assigned coordinates within a first camera coordinate system having a first projection center as origin and a first parallax shift relative to the instrument center. The method may include transforming the point cloud from the instrument coordinate system into the first camera coordinate system, resulting in a first transformed point cloud, detecting one or more uncovered points within the first transformed point cloud which are openly visible from the first projection center, and for each uncovered point, assigning a pixel value having corresponding coordinates in the first camera coordinate system.

Technologies are provided herein for modeling and tracking physical objects, such as human hands, within a field of view of a depth sensor. A sphere-mesh model of the physical object can be created and used to track the physical object in real-time. The sphere-mesh model comprises an explicit skeletal mesh and an implicit convolution surface generated based on the skeletal mesh. The skeletal mesh parameterizes the convolution surface and distances between points in data frames received from the depth sensor and the sphere-mesh model can be efficiently determined using the skeletal mesh. The sphere-mesh model can be automatically calibrated by dynamically adjusting positions and associated radii of vertices in the skeletal mesh to fit the convolution surface to a particular physical object.

A force sensed surface scanning system (20) employs a scanning robot (41) and a surface scanning controller (50). The scanning robot (41) includes a surface scanning end-effector (43) for generating force sensing data informative of a contact force applied by the surface scanning end-effector (43) to an anatomical organ. In operation, the surface scanning controller (50) controls a surface scanning of the anatomical organ by the surface scanning end-effector (43) including the surface scanning end-effector (43) generating the force sensing data, and further constructs an intraoperative volume model of the anatomical organ responsive to the force sensing data generated by the surface scanning end-effector (43) indicating a defined surface deformation offset of the anatomical organ.

An estimation device includes a memory and at least one processor. The at least one processor is configured to acquire information regarding a target object. The at least one processor is configured to estimate information regarding a location and a posture of a gripper relating to where the gripper is able to grasp the target object. The estimation is based on an output of a neural model having as an input the information regarding the target object. The estimated information regarding the posture includes information capable of expressing a rotation angle around a plurality of axes.

A sensing and computing system and method for capturing images and data regarding an object and calculating one or more parameters regarding the object using an internal, integrated CPU/GPU. The system comprises an imaging system, including a depth imaging system, color camera, and light source, that capture images of the object and sends data or signals relating to the images to the CPU/GPU, which performs calculations based on those signals/data according to pre-programmed algorithms to determine the parameters. The CPU/GPU and imaging system are contained within a protective housing. The CPU/GPU transmits information regarding the parameters, rather than raw data/signals, to one or more external devices to perform tasks in an industrial environment related to the object imaged.

The present invention relates to a light line triangulation apparatus with a measurement space for receiving a measurement object, a light projector, adapted to project a light line into the measurement space and/or onto the measurement object, an imager for detecting the light line in the measurement space, wherein the imager comprises imaging pixels arranged in a plurality of columns and rows. The apparatus of the invention is characterized in that the imager comprises multiple identical sets of polarization filters, wherein each set of polarization filters comprises at least two polarization filters with different polarization directions, wherein a respective polarization filter covers one of the columns.