Everted U-Net for 3D scene reconstruction and segmentation

Everted U-Net for 3D scene reconstruction and segmentation

Blog Article

Abstract.The field of data science related to the processing of three-dimensional objects is becoming more and more relevant.After dea eyewear the successes in image processing, the apotheosis of which was the development of generative neural net-works, the intensification of efforts in the direction of three-dimensional data processing looks logical.Although there are now numerous systems for the reconstruction of three-dimensional objects and other processing, almost all of the existing solutions are aimed at working with a single object.

The advances in image processing with neural networks have largely been made possible by huge datasets.There are also large datasets available for model training in this area.Freely avail-able datasets such as ShapeNet and ModelNet contain many thousands of different models, allowing for a high diversity of data.However, most of them provide single individual objects, which allows them to be used in tasks involving the processing of a single three-dimensional object, but when working with scenes containing many here objects, there is often a problem of finding an appropriate dataset.

This work is aimed at solving the problem of reconstruction and segmentation of three-dimensional scenes, as well as generation of datasets for the task of processing scenes from the real world.