Geometric compression for interactive transmission

International audienceThe compression of geometric structures is a relatively new field of data compression. Since about 1995, several articles have dealt with the coding of meshes, using for most of them the following approach: the vertices of the mesh are coded in an order that partially contains the topology of the mesh. In the same time, some simple rules attempt to predict the position of each vertex from the positions of its neighbors that have been previously coded. In this article, we describe a compression algorithm whose principle is completely different: the coding order of the vertices is used to compress their coordinates, and then the topology of the mesh is reconstructed from the vertices. This algorithm achieves compression ratios that are slightly better than those of the currently available algorithms, and moreover, it allows progressive and interactive transmission of the meshes

Dynamic Adaptive Point Cloud Streaming

High-quality point clouds have recently gained interest as an emerging form of representing immersive 3D graphics. Unfortunately, these 3D media are bulky and severely bandwidth intensive, which makes it difficult for streaming to resource-limited and mobile devices. This has called researchers to propose efficient and adaptive approaches for streaming of high-quality point clouds. In this paper, we run a pilot study towards dynamic adaptive point cloud streaming, and extend the concept of dynamic adaptive streaming over HTTP (DASH) towards DASH-PC, a dynamic adaptive bandwidth-efficient and view-aware point cloud streaming system. DASH-PC can tackle the huge bandwidth demands of dense point cloud streaming while at the same time can semantically link to human visual acuity to maintain high visual quality when needed. In order to describe the various quality representations, we propose multiple thinning approaches to spatially sub-sample point clouds in the 3D space, and design a DASH Media Presentation Description manifest specific for point cloud streaming. Our initial evaluations show that we can achieve significant bandwidth and performance improvement on dense point cloud streaming with minor negative quality impacts compared to the baseline scenario when no adaptations is applied.Comment: 6 pages, 23rd ACM Packet Video (PV'18) Workshop, June 12--15, 2018, Amsterdam, Netherland

Interactive 3D Visualization of a Large University Campus over the Web

Nowadays, with the rise and generalized use of web applications and graphical hardware evolution, one of the most interesting problems deals with realistic real-time visualization of virtual environments on web browsers. This paper shows an on-line application to dynamically visualize a large campus on the World Wide Web. The application focuses on a smooth walk through a large 3D environment in real-time as an alternative way to index geographically related information. This way, contents are continuously filtered based on viewpoint¿s position. This can be made thanks to the availability of different models corresponding to different levels of detail (LOD) for each modeled building. A server storage model has been purposed including all models, compound of meshes, textures and information. The technique is based on an algorithm that performs a progressive refining of the models, according to the distance from the viewpoint.Vendrell Vidal, E.; Sanchez Belenguer, C. (2011). Interactive 3D Visualization of a Large University Campus over the Web. International Journal of Computer Information Systems and Industrial Management Applications. 3:514-521. http://hdl.handle.net/10251/35020S514521

Lossless Compression of Predicted Floating-Point Geometry

The sizeof geometric data sets in scientific and industrial applications is constantly increasing. Storing surfng or volume meshes in standard uncompressedf ormats results in large files that are expensive to store and slow to load and transmit. Scientists and engineersofne refeer ff using mesh compression because currently available schemes modif the mesh data. While connectivity is encoded in a lossless manner, the floating-point coordinates associated with the vertices are quantized onto aunif6: integer grid to enable e#cient predictive compression. Although a fine enough grid can usually represent the data with su#cient precision, the original floating-point values will change, regardless of grid resolution. In this paper we describe a methodf or compressing floating-point coordinates with predictive coding in a completely lossless manner. The initial quantization step is omitted and predictions are calculated in floating-point. The predicted and the actual floating-point values are broken up into sign, exponent, and mantissa and their corrections are compressed separately with context-based arithmetic coding. As the quality of the predictions varies with the exponent, we use the exponent to switch between di#erent arithmetic contexts. We report compression results using the popular parallelogram predictor, but our approach will work with any prediction scheme. The achieved bit-ratesf or lossless floating-point compression nicely complement those resultingfsu unifting quantizing with di#erent precisions