#POINT CLOUD TEXTURE GENERATION MESHLAB UPDATE#

If there are holes in the mesh or occlusions, you can also use Tools->Mesh->Close Holes. After you've generated the mesh using the highest quality settings your computer's memory will allow, I usually use Tools->Mesh->View Mesh Statistics->Fix Topology to repair defects in the mesh. It's better to start with the most points possible in the dense point cloud and then decimate the mesh if necessary, rather than to start with a less dense point cloud and generate the mesh at the highest possible settings. If the image is too large, it might help to break the orthomosaic into blocks, which you can choose in the Export Orthophoto dialogue box.įirst, I'd generate the highest quality point cloud that your computer's memory is capable of using to generate a mesh (it's the step most demanding of RAM). The size of the pixels in the 'Export Orthomosaic' dialogue box is set by default to the highest possible resolution, so I wouldn't change these values. However, the resolution of the orthomosaic is partly dependent on the quality of the mesh. It's not necessary to generate texture for the mesh in order to generate an orthophoto. It seems to produce orthomosaics with fewer artifacts and better resolution, based on the results I've seen so far.

#POINT CLOUD TEXTURE GENERATION MESHLAB UPDATE#

If you haven't already, I'd recommend you update to the latest version of Photoscan (currently v. I have a mural project on-going as well and hope we can exchange information. We demonstrate the large-scale and high-precision, 3D building model generation approach on a campus.It sounds like a really interesting project. The experimental results show that MS3DQE-Net achieves a state-of-the-art performance in multi-source 3D data quality evaluation. Specifically, to our knowledge, we are the first researchers to propose such multi-source 3D dataset. To train MS3DQE-Net, a multi-source 3D dataset is constructed, which collected from a real scene based on mobile laser scanning (MLS) 3D point clouds and 3D mesh data, including pairs of matching 3D meshes and 3D point clouds of the 3D building model. The MS3DQE-Net uses 3D meshes and 3D point clouds as inputs and fuses the learned features to obtain a more complete shape description. Then, the evaluation results are utilized to guide 3D building model generation. First, A Multi-Source 3D data Quality Evaluation Network (MS3DQE-Net) is proposed for evaluating the quality of 3D meshes and 3D point clouds. In this paper, we propose a novel 3D building model generation method based on multi-source 3D data fusion of 3D point cloud data and 3D mesh data with deep learning method.

However, due to the limitations of single-source data, existing methods cannot simultaneously balance the local accuracy, overall integrity, and data scale of the building model. The high-precision generation of 3D building models is a controversial research topic in the field of smart cities. This approach provides more detailed information about the wear mechanism and can help either in roadheader cutting head diagnostics or to develop a strategy and optimize the toolpath for the numerically controlled hardfacing machine. The 3D scan of the worn out specimen is compared to the master model via algorithm developed by the authors. Those factors are: light level, presence of polarizing filter and the distance from the scanned object. An experiment of three factors on two levels is carried out to indicate the proper setup of the scanning rig to obtain plausible results. This paper presents a methodology used to create a 3D photogrammetric model of most of the commercially available tangential-rotary cutters in their before and after abrasive exploitation state. In order to automatize the regeneration, the wear rate assessment is necessary. Any mining facility could benefit from decreasing the cost along with a substantial durability increase of a conical pick thus, the hardfacing method of production and regeneration should be taken into account. The rapid wear of conical picks used in rock cutting heads in the mining industry has a significant economic impact in cost effectiveness for a given mineral extraction business.

Point cloud texture generation meshlab