Search

We present a generative model-based point cloud generation method using deep adversarial local features. The proposed generative adversarial network (GAN) can reduce computational load and increase the accuracy in three-dimensional (3D) acquisition, reconstruction, and rendering processes. To train the proposed GAN, we first optimize the latent space using an autoencoder to extract local features. The training process provides an accurate estimation of local context from the latent variables and robust point cloud generation. The main contribution of this work is a novel deep learning-based 3D point cloud generation, which significantly reduces computational load to render augmented reality (AR) and mixed reality (MR) contents. Additional contribution in the deep learning field is twofold: i) The autoencoder in the proposed network avoids the vanishing gradient problem using hierarchically linked features in different layers, and ii) the complexity of the network is significantly reduced by removing the transformation network that estimates the affine transformation matrix of the point cloud. [ABSTRACT FROM AUTHOR]

Advanced driver-assistance systems need a camera calibration algorithm for various vision applications including surround-view monitoring (SVM) and lane departure warning (LDW). Although cameras mounted on a vehicle are calibrated in the manufacturing process, their orientation angles are subject to tilting because of continuing vibration and external impact. To solve the problem, this paper presents an online calibration algorithm for camera orientation estimation using motion vectors and three-dimensional geometry. The proposed algorithm consists of three steps: i) driving direction estimation by calculating an intersection of motion vectors, ii) camera orientation estimation based on 3-line random sample consensus (RANSAC) using the estimated intersection, and iii) final orientation decision using extended Kalman filter from the result of each frame. Experimental results demonstrate that the proposed algorithm stably estimates camera orientation angles from motion vectors and lines under the parallelism and orthogonality assumptions. [ABSTRACT FROM AUTHOR]