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Pytorch framework for doing deep learning on point clouds
Torch Points 3D is a framework for developing and testing common deep learning models to solve tasks related to unstructured 3D spatial data i.e. Point Clouds. The framework currently integrates some of the best-published architectures and it integrates the most common public datasets for ease of reproducibility. It heavily relies on Pytorch Geometric and Facebook Hydra library thanks for the great work!
...-It uses Windows Forms as a graphical API for compatibility reasons with Emgu CV.
-All required libraries are included in the package to download.
Following releases will include:
- a DirectX viewer to allow 3D point cloud data and IFC file visualization and editing
- adjustment of processing rate of frames depending on buffer availability (send next frame serially or skip till buffer empties)
- selection of frames based on criteria (blur metric, etc.)
- introduction of error handling and help manual
- a scrollbar for video playback
PhotoCloud is a tool to browse and a navigate for datasets composed of (a) a collection of registered images and (b) a 3Dmodel over which images are aligned. The 3Dmodel can be a point-cloud, triangle mesh. PhotoCloud is based on a client/server architecture and it is scalable: very large dataset (both in terms of (a) and (b) can be handled.
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