A Neural Network for Simultaneously Reconstructing Transparent and Opaque Surfaces
نویسندگان
چکیده
This paper presents a neural network (NN) to recover threedimensional (3D) shape of an object from its multiple view images. The object may contain non-overlapping transparent and opaque surfaces. The challenge is to simultaneously reconstruct the transparent and opaque surfaces given only a limited number of views. By minimizing the pixel error between the output images of this NN and teacher images, we want to refine vertices position of an initial 3D polyhedron model to approximate the true shape of the object. For that purpose, we incorporate a ray tracing formulation into our NN’s mapping and learning. At the implementation stage, we develop a practical regularization learning method using texture mapping instead of ray tracing. By choosing an appropriate regularization parameter and optimizing using hierarchical learning and annealing strategies, our NN gives more approximate shape.
منابع مشابه
Simultaneously Reconstructing Transparent and Opaque Surfaces from Texture Images
This paper addresses the problem of reconstructing nonoverlapping transparent and opaque surfaces from multiple view images. The reconstruction is attained through progressive refinement of an initial 3D shape by minimizing the error between the images of the object and the initial 3D shape. The challenge is to simultaneously reconstruct both the transparent and opaque surfaces given only a lim...
متن کاملAnalytic Reconstruction of Transparent and Opaque Surfaces from Texture Images
This paper addresses the problem of reconstructing nonoverlapping transparent and opaque surfaces from multiple view images. The reconstruction is attained through progressive refinement of an initial 3D shape byminimizing the error between the images of the object and the initial 3D shape. The challenge is to simultaneously reconstruct both the transparent and opaque surfaces given only a limi...
متن کاملA Self-Reconstructing Algorithm for Single and Multiple-Sensor Fault Isolation Based on Auto-Associative Neural Networks
Recently different approaches have been developed in the field of sensor fault diagnostics based on Auto-Associative Neural Network (AANN). In this paper we present a novel algorithm called Self reconstructing Auto-Associative Neural Network (S-AANN) which is able to detect and isolate single faulty sensor via reconstruction. We have also extended the algorithm to be applicable in multiple faul...
متن کاملA Deep Model for Super-resolution Enhancement from a Single Image
This study presents a method to reconstruct a high-resolution image using a deep convolution neural network. We propose a deep model, entitled Deep Block Super Resolution (DBSR), by fusing the output features of a deep convolutional network and a shallow convolutional network. In this way, our model benefits from high frequency and low frequency features extracted from deep and shallow networks...
متن کامل