Apple Inc. (Cupertino, CA)

A system comprises an encoder configured to compress attribute data for points and / or an encoder designed to decompress the compressed attribute information. A compressed attribute file includes the attribute values for at minimum one point. Correction values for attribute attributes are also part of this compressed attribute file. At least in part attributes values from nearby points are used to determine attribute values. To find the values for attribute correction The predicted attribute values are compared with attributes values from a point cloud before compression. To improve computing efficiency and/or repeatability fixed-point representations are employed in determining expected attribute values and attribute correction values. Decoders use the same algorithm for predicting values as an encoder, and corrects predicted values with attribute correction values included in the compressed file of attribute information by using representations of fixed-point numbers.

Technical Field

This disclosure is of a general nature and is related to decompression or compression point clouds that contain a plurality points each with associated attributes.

Description of Related Art

There are many types of sensors, including light detection and range (LIDAR), 3-D-cameras, scanners and 3-D-cameras. may capture data indicating positions of points in three-dimensional space, for example locations in the X three, Y and Z planes. These systems can also capture attributes data, such as spatial information for each point, such as color information (e.g. RGB values), intensity attributes, reflectivity attributes, motion-related attributes, modalityattributes, and various other attributes. Additional attributes can be assigned to points under certain situations, for example, the date at which the point was made. These sensors can capture points and then create an “point cloud” that could comprise a collection of points with related spatial information as well as other attributes associated with them. In certain situations points clouds can include thousands of points, hundreds of thousands of points, millions of points, or more points. In some cases point clouds may be developed in software, rather than being captured by one or two sensors. Point clouds can contain large quantities of information, which can be expensive and time-consuming to store or transmit.

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