On the Benefit of Adversarial Training for Monocular Depth Estimation

1 code implementation • 29 Oct 2019 • Rick Groenendijk, Sezer Karaoglu, Theo Gevers, Thomas Mensink

For the quality of the image reconstruction and disparity prediction, a combination of different losses is used, including L1 image reconstruction losses and left-right disparity smoothness.

Depth Prediction Generative Adversarial Network +2

Multi-Loss Weighting with Coefficient of Variations

1 code implementation • 3 Sep 2020 • Rick Groenendijk, Sezer Karaoglu, Theo Gevers, Thomas Mensink

In this paper, we propose a weighting scheme based on the coefficient of variations and set the weights based on properties observed while training the model.

Monocular Depth Estimation Multi-Task Learning +1

MorphPool: Efficient Non-linear Pooling & Unpooling in CNNs

1 code implementation • 25 Nov 2022 • Rick Groenendijk, Leo Dorst, Theo Gevers

Pooling is essentially an operation from the field of Mathematical Morphology, with max pooling as a limited special case.

HaarNet: Large-scale Linear-Morphological Hybrid Network for RGB-D Semantic Segmentation

no code implementations • 11 Oct 2023 • Rick Groenendijk, Leo Dorst, Theo Gevers

In the network, morphological Haar sampling is applied to both feature channels in several layers, which splits extreme values and high-frequency information such that both can be processed to improve both modalities.

Semantic Segmentation