![]() ![]() However, once I arrived at the initial results of the work, I noticed that I had accidentally cut too much geometry in that zone. Initially, in this phase of the project I tried to cut the base of the sculpture precisely. This made it possible to select and cut the surface, yielding more linear borders. To do this, I used the Remeshing, Simplification and Reconstruction->Surbdivision surfaces: Midpoint filter to subdivide the larger triangles and decrease their size. To obtain a lower border that would be as even as possible, I performed remeshing before cutting. First of all, using the Select faces in a rectangular region and Delete selected faces and vertices tool, I eliminated the lower surface of the base on which the lion stands, which had been completely invented by the filter. Therefore, the model had to be cleaned to remove the superfluous geometry reconstructed by Poisson. Furthermore, the work had to be saved frequently, always keeping the “Normal” box ticked so that the point cloud would not lose the normals per vertex. This was delicate work that required a great attention to avoid creating holes in the point cloud. I then tried to remove the dark points distributed along certain areas of the surface as much as possible. For example, I deleted the vertices around the lion’s mouth. After completing an approximate deletion of many of the excess points around the lion, I continued with detailed cleaning aimed at sparing only the vertices related to the sculpture in the most precise way possible. ![]() Therefore, first of all I had to remove vertices not related to the sculpture using various selection and deletion tools and filters available in the program ( Select vertices, Delete vertices, Select->Invert selection). In the dense point cloud I reconstructed everything that could be observed in the photos, and thus an entire section of the room in which the lion was located. ![]() The next steps of the work to develop the model and process the data were conducted from this point on using MeshLab software, to which I uploaded the. ![]() Therefore, a set of 49 photos was uploaded using commercial software, AgiSoft Photoscan, which made it possible to obtain a dense point cloud suitable to continue the work. Even when I supplemented the set of pictures with details of the most problematic areas and attempted a dual reconstruction of the sculpture by dividing it into two parts, the same problems continued to arise and the gaps remained very evident. In fact, the front of the lion, which has extensive relief work, was reconstructed well, but the hindquarters and the sides of the belly and legs, which are very smooth, had big gaps. However, reconstruction with Visual SFM still did not yield satisfactory results. Therefore, I made several attempts with Visual SFM using composite sets from various photographs. Since the results were not satisfactory, I took more pictures of the sculpture to get better images (more in focus, with a more suitable white balance and better overlap of the sequences of pictures). With Visual SFM the Dense Stereo Matching processes can be followed step by step in four stages: Therefore, I tried again using Visual SFM software. However, the results were not satisfactory. A first attempt with Dense Stereo Matching starting with photographs was done using the Arc3D Web Service, which permits remote 3D reconstruction sent to the user by email. ![]()
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