Cultural heritage Point cloud scans be used to visualize and archive cultural heritage objects and collections. Examples are the Shipping Gallery in the London Museum [12]. Another example is the project Culture 3D Cloud [13] shown in Figure 4, where pictures taken by smart phones of cultural heritage objects are transformed into 3D point clouds that can be viewed from any angle. Point cloud compression and streaming can make these objects available to the wider public. Figure 4 Cultural heritage in the 3D Cloud project [13] Key requirements for this application are: to enable increasing quality. Progressive coding attributes coding is needed, preferable 8-12 bits per component Color attributes coding such as for material properties. Generic is important to enable the best representation when possible Lossless Typical point clouds in this use case have the following characteristics: 1 Million upto billions of points (e.g. [13]) Color attributes of 8-12 bits per color component Can contain multiple clusters/groups of points Autonomous Navigation Based on Large-Scale 3D Dynamic Maps Large-scale 3D maps of indoor and outdoor environments can be created using devices that provide localization combined with depth and color measurements of the surrounding environment. Localization could be achieved with GPS, inertial measurement units (IMU), cameras, or combinations of these and other devices, while the depth measurements could be achieved with time-of-flight, radar or laser scanning systems. Example mapping systems are already commercially available and come in various forms such as the high-end mobile mapping system [16] shown in Figure 5(a). There are also lightweight mobile platforms under development based on sensors mounted on drones or in mobile phones/tablets. Irrespective of the specific platform that is used to acquire the measurements, it is possible to generate a 3D map by combining the depth measurements, such as the high-density point cloud in Figure 5(b), with camera images. This combination of point cloud data with camera images to generate a 3D map is illustrated in Figure 5(c). These maps can further be combined with road markings such as lane information and road signs to create maps to enable autonomous navigation of vehicles around a city as shown in Figure 5(d). Multiple map layers will be stored and exchanged across the network, including static maps that do not change very frequently and dynamic maps that include real-time information about dynamic objects in the scene such as vehicles or pedestrians. (a) (b)
Cultural heritage Point cloud scans be used to visualize and archive cultural heritage objects and collections. Examples are the Shipping Gallery in the London Museum [12]. Another example is the project Culture 3D Cloud [13] shown in Figure 4, where pictures taken by smart phones of cultural heritage objects are transformed into 3D point clouds that can be viewed from any angle. Point cloud compression and streaming can make these objects available to the wider public. Figure 4 Cultural heritage in the 3D Cloud project [13] Key requirements for this application are: Progressive coding to enable increasing quality. Color attributes coding is needed, preferable 8-12 bits per component Generic attributes coding such as for material properties. Lossless is important to enable the best representation when possible Typical point clouds in this use case have the following characteristics: 1 Million upto billions of points (e.g. [13]) Color attributes of 8-12 bits per color component Can contain multiple clusters/groups of points Autonomous Navigation Based on Large-Scale 3D Dynamic Maps Large-scale 3D maps of indoor and outdoor environments can be created using devices that provide localization combined with depth and color measurements of the surrounding environment. Localization could be achieved with GPS, inertial measurement units (IMU), cameras, or combinations of these and other devices, while the depth measurements could be achieved with time-of-flight, radar or laser scanning systems. Example mapping systems are already commercially available and come in various forms such as the high-end mobile mapping system [16] shown in Figure 5(a). There are also lightweight mobile platforms under development based on sensors mounted on drones or in mobile phones/tablets. Irrespective of the specific platform that is used to acquire the measurements, it is possible to generate a 3D map by combining the depth measurements, such as the high-density point cloud in Figure 5(b), with camera images. This combination of point cloud data with camera images to generate a 3D map is illustrated in Figure 5(c). These maps can further be combined with road markings such as lane information and road signs to create maps to enable autonomous navigation of vehicles around a city as shown in Figure 5(d). Multiple map layers will be stored and exchanged across the network, including static maps that do not change very frequently and dynamic maps that include real-time information about dynamic objects in the scene such as vehicles or pedestrians. (a) (b)
w16331_Use_Cases_for_Point_Cloud_Compression_%28PCC%29_0.docx