Corpus ID: 203593276

Depth Estimation in Nighttime using Stereo-Consistent Cyclic Translations

  title={Depth Estimation in Nighttime using Stereo-Consistent Cyclic Translations},
  author={Aashish Sharma and Robby T. Tan and Loong Fah Cheong},
Most existing methods of depth from stereo are designed for daytime scenes, where the lighting can be assumed to be sufficiently bright and more or less uniform. Unfortunately, this assumption does not hold for nighttime scenes, causing the existing methods to be erroneous when deployed in nighttime. Nighttime is not only about low light, but also about glow, glare, non-uniform distribution of light, etc. One of the possible solutions is to train a network on nighttime images in a fully… Expand
Optical Flow in Dense Foggy Scenes Using Semi-Supervised Learning
A semi-supervised deep learning technique that employs real fog images without optical flow ground-truths in the training process, which outperforms the state-of-the-art methods in estimating optical flow in dense foggy scenes and proposes a new training strategy that combines supervised synthetic-data training and unsupervised real- data training. Expand
DeFeat-Net: General Monocular Depth via Simultaneous Unsupervised Representation Learning
DeFeat-Net (Depth & Feature network), an approach to simultaneously learn a cross-domain dense feature representation, alongside a robust depth-estimation framework based on warped feature consistency, to outperform the current state of the art in monocular depth estimation and supervised feature representation learning. Expand


Night-to-Day Image Translation for Retrieval-based Localization
This paper proposes ToDayGAN – a modified image-translation model to alter nighttime driving images to a more useful daytime representation, and improves localization performance by over 250% compared the current state-of-the-art, in the context of standard metrics in multiple categories. Expand
Into the Twilight Zone: Depth Estimation Using Joint Structure-Stereo Optimization
This work proposes to rely on structures comprising of piecewise constant regions and principal edges in the given image, as these are the important regions for extracting disparity information, and judiciously retains the coarser textures for stereo matching. Expand
Joint Image Denoising and Disparity Estimation via Stereo Structure PCA and Noise-Tolerant Cost
A new joint framework is proposed that iteratively optimizes these two different tasks in a multiscale fashion and achieves superior performance than the state-of-the-art image denoising and disparity estimation methods. Expand
Zoom and Learn: Generalizing Deep Stereo Matching to Novel Domains
This work proposes a self-adaptation approach for CNN training, utilizing both synthetic training data and stereo pairs in the new domain (without ground-truths), and forms an iterative optimization problem with graph Laplacian regularization. Expand
Simultaneous Depth Reconstruction and Restoration of Noisy Stereo Images using Non-local Pixel Distribution
A new robust stereo algorithm to noise is presented that performs the stereo matching and the image de-noising simultaneously and is more robust and accurate than other conventional algorithms in both stereo matchingand denoising. Expand
LIME: Low-Light Image Enhancement via Illumination Map Estimation
Experiments on a number of challenging low-light images are present to reveal the efficacy of the proposed LIME and show its superiority over several state-of-the-arts in terms of enhancement quality and efficiency. Expand
Evaluation of Stereo Matching Costs on Images with Radiometric Differences
Among the best costs are BilSub, which performs consistently very well for low radiometric differences; HMI, which is slightly better as pixelwise matching cost in some cases and for strong image noise; and Census, which showed the best and most robust overall performance. Expand
Pyramid Stereo Matching Network
PSMNet is a pyramid stereo matching network consisting of two main modules: spatial pyramid pooling and 3D CNN, which takes advantage of the capacity of global context information by aggregating context in different scales and locations to form a cost volume. Expand
A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation
  • N. Mayer, Eddy Ilg, +4 authors T. Brox
  • Computer Science, Mathematics
  • 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2016
This paper proposes three synthetic stereo video datasets with sufficient realism, variation, and size to successfully train large networks and presents a convolutional network for real-time disparity estimation that provides state-of-the-art results. Expand
The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes
This paper generates a synthetic collection of diverse urban images, named SYNTHIA, with automatically generated class annotations, and conducts experiments with DCNNs that show how the inclusion of SYnTHIA in the training stage significantly improves performance on the semantic segmentation task. Expand