Recently, machine-learning-inspired methods have been proposed to automatically estimate the depth map of a single monocular image by applying image parsing. The proposed methods carry the “big data” philosophy of machine learning. They apply to arbitrary scenes and require no manual explanation. Two types of methods are proposed.

Our approach is to train the Deep Convolutional Network (CNN) on stereo pairs of images which are extracted from 3D movies. The Deep CNN outputs the depth map using a single image as input. This depth map can be combined with the input image to produce the corresponding stereo image of the input image. KEYWORDS: Machine Learning, Virtual ...

This paper compares the existing techniques and proposes a MeshCNN-based nove1 technique to convert 2D images into 3D images. The publicly available dataset namely VisDrone is used for experimental study. The proposed technique has been validated against the existing techniques.

Easier problem –reconstruct 3D model from a 3D model. Using convolutional neural network (CNN). Peter Naftaliev - 2d3d.ai Jun-20

Abstract: We present a multi-scale deep convolutional neural network (CNN) for the task of automatic 2D-to-3D conversion. Traditional methods, which make a virtual view from a reference view, consist of separate stages i.e., depth (or disparity) estimation for the reference image and depth image-based rendering (DIBR) with estimated depth.

This section reviews the literature on different methods associated with 2D to 3D image conversion. It provides insights on various aspects of conversion methods including machine learning and deep learning. 3.1 Image Conversion Using Deep Learning Deep learning models are widely used for processing image data. Addressed challenges in

We develop a simplified and computationally-efficient version of our recent 2D-to-3D image conversion algorithm. Given a repository of 3D images, either as stereopairs or image+depth pairs, we find k pairs whose photometric content most closely matches that of a …

The approach in this paper is to use CNN to build a 3D model from multiple 2D images. The generative model is divided into three different modules. The first module consists of 2D-CNN which will extract features from input 2D images, the second module consists of attention activation function which calculates attention

Feb 28, 2020 · Given a standard RGB image, the 3D Photos CNN can estimate a distance from the camera for each pixel. We accomplished this through four means: A network architecture built with a set of parameterizable, mobile-optimized neural building blocks.

This proposes an end-to-end deep learning method for digitizing highly detailed clothed humans that can infer both 3D surface and texture from a single image, and optionally, multiple input images. Highly intricate shapes, such as hairstyles, clothing, as well as their variations and deformations can be digitized in a unified way.