Generative adversarial framework for depth filling via Wasserstein metric, cosine transform and domain transfer. (July 2019)

• Record Type: Journal Article
• Authors: Atapour-Abarghouei, Amir; Akcay, Samet; Payen de La Garanderie, Grégoire; Breckon, Toby P.
• Is Part Of: Pattern recognition. Volume 91(2019:Jul.)
• Page Start: 232
• Page End: 244
• Publication Date: 2019-07
• Subjects: Depth image -- Hole filling -- Self-supervised learning -- Generative model -- Adversarial training -- Feature distance -- Domain adaptation; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2019.02.010
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: Depth completion can be performed by learning the context and contents of the scene. Depth holes can be predicted based on the scene and capture device characteristics. Using absolute deviations loss in frequency domain (DCT) improves reconstruction. Adversarial training (Wasserstein metric) can improve training and mode selection. Using domain transfer, models trained on synthetic data are used in the real world. Graphical abstract: Abstract: In this work, the issue of depth filling is addressed using a self-supervised feature learning model that predicts missing depth pixel values based on the context and structure of the scene. A fully-convolutional generative model is conditioned on the available depth information and full RGB colour information from the scene and trained in an adversarial fashion to complete scene depth. Since ground truth depth is not readily available, synthetic data is instead used with a separate model developed to predict where holes would appear in a sensed (non-synthetic) depth image based on the contents of the RGB image. The resulting synthetic data with realistic holes is utilized in training the depth filling model which makes joint use of a reconstruction loss which employs the Discrete Cosine Transform for more realistic outputs, an adversarial loss which measures the distribution distances via the Wasserstein metric and a bottleneck feature loss that aids in better contextual feature execration. Additionally, the model is adversarially adapted to perform well on naturally-obtained data with no available ground truth. Qualitative and quantitative evaluations demonstrate the efficacy of the approach compared to contemporary depth filling techniques. The strength of the feature learning capabilities of the resulting deep network model is also demonstrated by performing the task of monocular depth estimation using our pre-trained depth hole filling model as the initialization for subsequent transfer learning.