Contribution: This paper offers a general framework of technology development which allows for (a) a systematic approach to analyze technologies’ main components, strengths, limitations and forward paths; (b) a better understanding by en- gineers of their practices and their projects; and (c) a clearer way to convey the engineering enterprise to students, non-engineering stakeholders in projects and the general public. Background: Technology has been an immense force of material, economical and societal change in the last centuries. Several authors, such as Marshall McLuhan and Kevin Kelly, developed important ideas related to technology and the engineering process, but there seem to remain gaps between these theories to be explored, bridged and extrapolated. Research questions: (1) What are engineers’ aims in developing technologies? (2) What are technology’s main components, and its relationships? (3) What differentiates the engineering enterprise from others? Methodology: Integrate previous theories of technology, apply more technical concepts that were not addressed by writers who did not come from an engineering background, and derive consequences from the proposed methods. Findings: A proper theory of technology can better guide engineers from several branches to understand their practices and the fruits of their labor. It also aids in explaining these points to stakeholders, students and the general public.

We present a method to super-resolve voxelized point clouds down-sampled by a fractional factor, using look-up-tables (LUT) constructed from self-similarities from its own down-sampled neighborhoods. Given a down-sampled point cloud geometry Vd, and its corresponding fractional down-sampling factor s, the proposed method determines the set of positions that may have generated Vd, and estimates which of these positions were indeed occupied (super-resolution). Assuming that the geometry of a point cloud is approximately self-similar at different scales, LUTs relating down-sampled neighborhood configurations with children occupancy configurations can be estimated by further down-sampling the input point cloud to Vd2 , and by taking into account the irregular children distribution derived from fractional down-sampling. For completeness, we also interpolate texture by averaging colors from adjacent neighbors. We present extensive test results over different point clouds, showing the effectiveness of the proposed method against baseline methods.

Geometry-based point cloud compression (G-PCC) has been rapidly evolving in the context of international standards. Despite the inherent scalability of octree-based geometry description, current G-PCC attribute compression techniques prevent full scalability for compressed point clouds. In this paper, we present a solution to add scalability to attributes compressed using the region-adaptive hierarchical transform (RAHT), enabling the reconstruction of the point cloud using only a portion of the original bitstream. Without the full geometry information, one cannot compute the weights in which the RAHT relies on to calculate its coefficients for further levels of detail. In order to overcome this problem, we propose a linear relationship approximation relating the downsampled point cloud to the truncated inverse RAHT coefficients at that same level. The linear relationship parameters are sent as side information. After truncating the bitstream at a point corresponding to a given octree level, we can, then, recreate the attributes at that level. Tests were carried out and results attest the good approximation quality of the proposed technique.