
This paper presents a space-efficient alternative for visualizing 3D data; it utilizes topographical maps to allow easier editing and viewing of 3D models. It highlights applications in generating progressive level of detail (LODs) and offers a straightforward way to generate preview models that allow users interaction. This offers a geometrically-accurate model compared to more commonly used representations for such tasks (e.g. point clouds). The topographical model is achieved using incremental slicing in each of the dimensions resulting in a lighter, more uniform shape. In our testing, we observed an improved 35.4% in memory usage, 7 times faster rendering time, and 82.4% in space savings on average compared to mesh, wireframe, and point cloud. We also provide a browser-based demo that exports standard GLB files.

In this paper, a subjective quality based comparison between four point clouds codecs is presented. For that, a set of six point clouds was chosen. They were coded with four different point cloud encoding solutions, notably the MPEG V-PCC and G-PCC, a deep learning coding solution RS-DLPCC and also Draco, with different bit rates. A subjective test where the distorted and reference point clouds were rotated in a video sequence side by side followed by the quality evaluation, was conducted. Then the performance of a set of four point cloud objective quality metrics of he quality, was analysed using the subjective quality evaluation results. These metrics are usually reported as providing a good representation and are often used to evaluate compression solutions. In fact, the studied metrics tend to provide a good representation for V-PCC and G-PCC, an acceptable representation for RS-DLPCC, and a bad representation for Draco. It was also concluded that V-PCC is the best codec of the studied ones. The deep learning based solution still performs worst than the two MPEG codecs.