4 research outputs found
A Deep Learning based Fast Signed Distance Map Generation
Signed distance map (SDM) is a common representation of surfaces in medical
image analysis and machine learning. The computational complexity of SDM for 3D
parametric shapes is often a bottleneck in many applications, thus limiting
their interest. In this paper, we propose a learning based SDM generation
neural network which is demonstrated on a tridimensional cochlea shape model
parameterized by 4 shape parameters. The proposed SDM Neural Network generates
a cochlea signed distance map depending on four input parameters and we show
that the deep learning approach leads to a 60 fold improvement in the time of
computation compared to more classical SDM generation methods. Therefore, the
proposed approach achieves a good trade-off between accuracy and efficiency
A Deep Learning based Fast Signed Distance Map Generation
International audienceSigned distance map (SDM) is a common representation of surfaces in medical image analysis and machine learning. The computational complexity of SDM for 3D parametric shapes is often a bottleneck in many applications, thus limiting their interest. In this paper, we propose a learning based SDM generation neural network which is demonstrated on a tridimensional cochlea shape model parameterized by 4 shape parameters. The proposed SDM Neural Network generates a cochlea signed distance map depending on four input parameters and we show that the deep learning approach leads to a 60 fold improvement in the time of computation compared to more classical SDM generation methods. Therefore, the proposed approach achieves a good trade-off between accuracy and efficiency
3D Scene Reconstruction from a Single Viewport
We present a novel approach to infer volumetric reconstructions from a single viewport, based only on an RGB image and a reconstructed normal image. To overcome the problem of reconstructing regions in 3D that are occluded in the 2D image, we propose to learn this information from synthetically generated high-resolution data. To do this, we introduce a deep network architecture that is specifically designed for volumetric TSDF data by featuring a specific tree net architecture. Our framework can handle a 3D resolution of 512Âł by introducing a dedicated compression technique based on a modified autoencoder. Furthermore, we introduce a novel loss shaping technique for 3D data that guides the learning process towards regions where free and occupied space are close to each other. As we show in experiments on synthetic and realistic benchmark data, this leads to very good reconstruction results, both visually and in terms of quantitative measures