Abstract: A method, computer program, and computer system is provided for point cloud coding. The method includes receiving, from a bitstream, data corresponding to a point cloud; reconstructing, based on the data, a first attribute value of a first duplicate point from among a plurality of duplicate points corresponding to a single geometry position; obtaining at least one prediction residual corresponding to at least one remaining attribute value of at least one remaining duplicate point from among the plurality of duplicate points; reconstructing the at least one remaining attribute value based on the reconstructed first attribute and the at least one prediction residual; and decoding the data corresponding to the point cloud based on the reconstructed first attribute value and the reconstructed at least one remaining attribute value.

Abstract: A method, computer program, and computer system is provided for decoding point cloud data. Data corresponding to a point cloud is received. A number of contexts associated with the received data is reduced based on reducing a size of an array corresponding to syntax elements for predictive tree-based coding of the point cloud. The data corresponding to the point cloud is decoded based on the reduced number of contexts.

Abstract: A method, computer program, and computer system is provided for point cloud coding. Data corresponding to a point cloud is received. Hash elements corresponding to attribute values associated with the received data is reconstructed. A size of a hash table may be decreased based on deleting one or more of the hash elements corresponding to non-border regions associated with the attribute values. The data corresponding to the point cloud is decoded based on the reconstructed hash elements.

Abstract: A method, computer program, and computer system is provided for point cloud coding. The method includes receiving, from a bitstream, data corresponding to a point cloud; obtaining from the data a first prediction residual of a first component from among a plurality of components of an attribute associated with the point cloud; reconstructing the first prediction residual; determining a predicted second prediction residual based on the reconstructed first prediction residual and at least one model parameter; obtaining a second prediction residual of a second component from among the plurality of components based on the predicted second prediction residual; reconstructing the second prediction residual; and decoding the data corresponding to the point cloud based on the reconstructed first prediction residual and the reconstructed second prediction residual.

Abstract: A point stabilization control method includes determining a first displacement of the robot relative to the target object according to a position of the target object and an initial position of the robot; determining a to-be-offset displacement according to the first displacement and a second displacement of the target object relative to a target point and determining a target motion rate and a target motion direction of the robot according to the to-be-offset displacement and a mapping relation of a motion rate, a motion direction and the to-be-offset displacement; determining a current position of the robot according to the target motion rate and the target motion direction; and using the current position as the initial position and returning to the preceding steps until a distance between the current position of the robot and a position of the target point is less than a preset threshold.

Abstract: A method of decoding encoded information of a point cloud may be performed by at least one processor and comprises: obtaining an encoded bitstream, the encoded bitstream including encoded information of a point cloud including a set of points in a three-dimensional space; and determining a type of partitioning used to encode the information of the point cloud by at least one of parsing signals of at least three binary syntaxes or inferring at least one syntax of the at least three binary syntaxes.

Abstract: This application relates to uses of antibodies against fibroblast growth factor receptor 2 (FGFR2), including antibodies against the FGFR2 isoform FGFR2-IIIb (also known as FGFR2b), in treatment of certain cancers in combinations with mFOLFOX6 chemotherapy.

Abstract: Aspects of the disclosure provide methods, apparatuses, and a non-transitory computer-readable medium for point cloud compression and decompression. In a method, syntax information of a point cloud is decoded from a coded bitstream. The syntax information indicates that parallel decoding is to be performed on occupancy codes of nodes in a range of one or more partitioning depths in an octree partitioning structure of the point cloud is determined. The parallel decoding is performed on the occupancy codes of the nodes. The point cloud is reconstructed based on the occupancy codes of the nodes.

Abstract: An apparatus for point cloud decoding includes processing circuitry. The processing circuitry receives, from a coded bitstream for a point cloud, encoded occupancy codes for nodes in an octree structure for the point cloud. The nodes in the octree structure correspond to three dimensional (3D) partitions of a space of the point cloud. Sizes of the nodes are associated with sizes of the corresponding 3D partitions. Further, the processing circuitry decodes, from the encoded occupancy codes, occupancy codes for the nodes. At least a first occupancy code for a child node of a first node is decoded without waiting for a decoding of a second occupancy code for a second node having a same node size as the first node. Then, the processing circuitry reconstructs the octree structure based on the decoded occupancy codes for the nodes, and reconstructs the point cloud based on the octree structure.

Abstract: A method, computer program, and computer system is provided for processing point cloud data. Quantized point cloud data including a plurality of voxels is received. An occupancy map is generated for the quantized point cloud corresponding to lost voxels during quantization from among the plurality of voxels. A point cloud is reconstructed from the quantized point cloud data based on populating the lost voxels.

Abstract: An apparatus for point cloud decoding includes processing circuitry. The processing circuitry receives, from a coded bitstream for a point cloud, encoded occupancy codes for nodes in an octree structure for the point cloud. The nodes in the octree structure correspond to three dimensional (3D) partitions of a space of the point cloud. Sizes of the nodes are associated with sizes of the corresponding 3D partitions. Further, the processing circuitry decodes, from the encoded occupancy codes, occupancy codes for the nodes. At least a first occupancy code for a child node of a first node is decoded without waiting for a decoding of a second occupancy code for a second node having a same node size as the first node. Then, the processing circuitry reconstructs the octree structure based on the decoded occupancy codes for the nodes, and reconstructs the point cloud based on the octree structure.

Abstract: An electronic device is provided. The electronic device includes a frame, a sliding seat, and a receiver accommodated in the sliding seat. The frame includes a pair of side walls oppositely disposed and a top wall. The top wall defines an accommodating groove. The accommodating groove extends through the pair of side walls. The sliding seat is slidably disposed in the accommodating groove of the frame. The sliding seat defines a first receiving hole, and the receiver is sealed and covers the first receiving hole of the sliding seat, whereby a sound signal of the receiver is transmitted to outside through the first receiving hole when the sliding seat extends from the accommodating groove. In addition, a terminal device and a method for controlling an electronic device are provided.

Abstract: A method, computer program, and computer system for point cloud coding is provided. Data corresponding to a point cloud is received, and one or more geometric features are detected from among the data corresponding to the point cloud. A representation is determined for one or more of the detected geometric features, and the received data is encoded or decoded based on the determined representations whereby the point cloud is reconstructed based on the decoded data.

Abstract: A method, computer program, and computer system is provided for decoding point cloud data. Data corresponding to a point cloud is received. A number of contexts associated with the received data is reduced based on occupancy data corresponding to one or more parent nodes and one or more child nodes within the received data. The data corresponding to the point cloud is decoded based on the reduced number of contexts.

Abstract: A method, computer program, and computer system is provided for decoding point cloud data. Data corresponding to a point cloud is received. Hash elements corresponding to one or more neighboring nodes associated with a current node are identified. A size of a hash table is decreased based on deleting one or more of the hash elements corresponding to non-border regions of the one or more neighboring nodes. The data corresponding to the point cloud is decoded based on the hash table having the decreased size.

Abstract: Provided are a terminal device and a camera assembly thereof. The camera assembly comprises a fixing support, a camera module, a driving mechanism and an operating mechanism, wherein the camera module is connected to the fixing support in a rotatable manner, the driving mechanism is provided between the camera module and the fixing support and is used for producing a rotation driving force for the camera module relative to the fixing support so as to drive the camera module to rotate, with a side of the camera module as the axis, in such a manner that the opposite side of the camera module rotates away from the fixing support; and the operating mechanism is fixed on the fixing support and is used for limiting a reset state position of the camera module.

Abstract: The present invention discloses an overhead door hinge mechanism, comprising a hinge box boy, wherein the side wall of the hinge box body is provided with a telescopic arm assembly used for connecting with the door backplane of an overhead door, the telescopic arm assembly is provided with a two-stage rotating arm mechanism used for driving the door backplane to upwards flip over the range of a right angle, a connecting and rotating block and a spring stress block are rotationally fixed in the hinge box body, the telescopic arm assembly is rotationally fixed on the side wall of the hinge box body through the connecting and rotating block. The present invention can enable the overhead door to flip over a right angle and reduce the mechanical loss of the spring through the clamping mechanism, and has stable supporting structure and long service life.

Abstract: Described herein are methods and systems for remote visualization of real-time three-dimensional (3D) facial animation with synchronized voice. A sensor captures frames of a face of a person, each frame comprising color images of the face, depth maps of the face, voice data associated with the person, and a timestamp. The sensor generates a 3D face model of the person using the depth maps. A computing device receives the frames of the face and the 3D face model. The computing device preprocesses the 3D face model. For each frame, the computing device: detects facial landmarks using the color images; matches the 3D face model to the depth maps using non-rigid registration; updates a texture on a front part of the 3D face model using the color images; synchronizes the 3D face model with a segment of the voice data using the timestamp; and transmits the synchronized 3D face model and voice data to a remote device.

Abstract: A method and a system for training a machine learning algorithm (MLA) for object classification. The machine learning algorithm includes an embedding layer and a classification layer. A set of embedding indices representing a reference object is received. The set of embedding indices has been generated based on a byte representation of the reference object. A label associated with the reference object indicative of a reference class the objects belongs to is received. The MLA is iteratively trained to classify objects by embedding the set of embedding indices to obtain an input vector and by predicting an estimated class based on the input vector, and updating a parameter of at least one of the embedding layer and the updated embedding layer. The set of embedding indices is generated by parsing the byte representation to obtain byte n-grams and by applying a hash function on the byte n-grams.

Abstract: Described herein are methods and systems for remote visualization of three-dimensional (3D) animation. A sensor of a mobile device captures scans of non-rigid objects in a scene, each scan comprising a depth map and a color image. A server receives a first set of scans from the mobile device and reconstructs an initial model of the non-rigid objects using the first set of scans. The server receives a second set of scans. For each scan in the second set of one or more scans, the server determines an initial alignment between the depth map and the initial model. The server converts the depth map into a coordinate system of the initial model, and determines a displacement between the depth map and the initial model. The server deforms the initial model to the depth map using the displacement, and applies a texture to at least a portion of the deformed model.