4,319 research outputs found
Real-time self-adaptive deep stereo
Deep convolutional neural networks trained end-to-end are the
state-of-the-art methods to regress dense disparity maps from stereo pairs.
These models, however, suffer from a notable decrease in accuracy when exposed
to scenarios significantly different from the training set, e.g., real vs
synthetic images, etc.). We argue that it is extremely unlikely to gather
enough samples to achieve effective training/tuning in any target domain, thus
making this setup impractical for many applications. Instead, we propose to
perform unsupervised and continuous online adaptation of a deep stereo network,
which allows for preserving its accuracy in any environment. However, this
strategy is extremely computationally demanding and thus prevents real-time
inference. We address this issue introducing a new lightweight, yet effective,
deep stereo architecture, Modularly ADaptive Network (MADNet) and developing a
Modular ADaptation (MAD) algorithm, which independently trains sub-portions of
the network. By deploying MADNet together with MAD we introduce the first
real-time self-adaptive deep stereo system enabling competitive performance on
heterogeneous datasets.Comment: Accepted at CVPR2019 as oral presentation. Code Available
https://github.com/CVLAB-Unibo/Real-time-self-adaptive-deep-stere
Unsupervised Adversarial Depth Estimation using Cycled Generative Networks
While recent deep monocular depth estimation approaches based on supervised
regression have achieved remarkable performance, costly ground truth
annotations are required during training. To cope with this issue, in this
paper we present a novel unsupervised deep learning approach for predicting
depth maps and show that the depth estimation task can be effectively tackled
within an adversarial learning framework. Specifically, we propose a deep
generative network that learns to predict the correspondence field i.e. the
disparity map between two image views in a calibrated stereo camera setting.
The proposed architecture consists of two generative sub-networks jointly
trained with adversarial learning for reconstructing the disparity map and
organized in a cycle such as to provide mutual constraints and supervision to
each other. Extensive experiments on the publicly available datasets KITTI and
Cityscapes demonstrate the effectiveness of the proposed model and competitive
results with state of the art methods. The code and trained model are available
on https://github.com/andrea-pilzer/unsup-stereo-depthGAN.Comment: To appear in 3DV 2018. Code is available on GitHu
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