10,023 research outputs found
CNN-SLAM: Real-time dense monocular SLAM with learned depth prediction
Given the recent advances in depth prediction from Convolutional Neural
Networks (CNNs), this paper investigates how predicted depth maps from a deep
neural network can be deployed for accurate and dense monocular reconstruction.
We propose a method where CNN-predicted dense depth maps are naturally fused
together with depth measurements obtained from direct monocular SLAM. Our
fusion scheme privileges depth prediction in image locations where monocular
SLAM approaches tend to fail, e.g. along low-textured regions, and vice-versa.
We demonstrate the use of depth prediction for estimating the absolute scale of
the reconstruction, hence overcoming one of the major limitations of monocular
SLAM. Finally, we propose a framework to efficiently fuse semantic labels,
obtained from a single frame, with dense SLAM, yielding semantically coherent
scene reconstruction from a single view. Evaluation results on two benchmark
datasets show the robustness and accuracy of our approach.Comment: 10 pages, 6 figures, IEEE Computer Society Conference on Computer
Vision and Pattern Recognition (CVPR), Hawaii, USA, June, 2017. The first two
authors contribute equally to this pape
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
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