A number of important applied problems in engineering, finance and medicine can be In the last five years, advances in deep learning have led to significant progress in allowing computers to understand the real world from visual input, opening up many new opportunities for dramatic progress in areas ranging from robotics to virtual and augmented reality, as well as medical and industry 4.0 applications.
Most of these machine learning architectures are based on convolutional neural networks (CNNs), which are able to learn powerful features from images, and even generate highly realistic pictures from scratch using generative adversarial networks (GANs). In particular, for images, we have seen tremendous success in both discriminative and generative tasks.
Unfortunately, for 3D data, e.g., data obtained from 3D scans of various types, research remains in its infancy. Developing machine learning techniques for this type of data has great potential, as our world is inherently three-dimensional, and even four-dimensional when considering the temporal domain.
Performing scene understanding in 3D has significant advantages; for instance, a machine learning approach does not need to learn viewpoint invariance, and as a consequence, requires less training data.
However, this additional third dimension (and fourth for dynamics) comes at significant computational and memory overhead, which has so far been the major bottleneck in applying machine learning to 3D.
- In this project, we address these shortcomings by developing efficient machine learning algorithms for 3D and 4D data analysis in the context of several important geometry processing tasks.
- In particular, we will develop deep learning architectures and training methods capable of efficiently modeling different types of static and dynamic 3D data representations, including sparse spatial and temporal representations on voxel volumes, RGB-D images, point clouds, multi-view images, and meshes.
- We will further construct new datasets designed for our scenarios, captured from the real-world, as well as synthetically generated with simulated renderings, augmented to reduce the reality gap between artificial and real data.
- Finally, we will develop new neural network architectures designed for discriminative and generative applications embedded in spatial, and specifically temporal, domains.
In order to showcase our learning methods, we will apply them to static and dynamic 3D reconstruction tasks, as well as semantic scene understanding in 3D and 4D with an emphasis on fusing the spatial and temporal domains.
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- R. Klokov and V. Lempitsky. Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models. IEEE International Conference on Computer Vision (ICCV), 2017.
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