57 research outputs found
SRFNet: Monocular Depth Estimation with Fine-grained Structure via Spatial Reliability-oriented Fusion of Frames and Events
Monocular depth estimation is a crucial task to measure distance relative to
a camera, which is important for applications, such as robot navigation and
self-driving. Traditional frame-based methods suffer from performance drops due
to the limited dynamic range and motion blur. Therefore, recent works leverage
novel event cameras to complement or guide the frame modality via frame-event
feature fusion. However, event streams exhibit spatial sparsity, leaving some
areas unperceived, especially in regions with marginal light changes.
Therefore, direct fusion methods, e.g., RAMNet, often ignore the contribution
of the most confident regions of each modality. This leads to structural
ambiguity in the modality fusion process, thus degrading the depth estimation
performance. In this paper, we propose a novel Spatial Reliability-oriented
Fusion Network (SRFNet), that can estimate depth with fine-grained structure at
both daytime and nighttime. Our method consists of two key technical
components. Firstly, we propose an attention-based interactive fusion (AIF)
module that applies spatial priors of events and frames as the initial masks
and learns the consensus regions to guide the inter-modal feature fusion. The
fused feature are then fed back to enhance the frame and event feature
learning. Meanwhile, it utilizes an output head to generate a fused mask, which
is iteratively updated for learning consensual spatial priors. Secondly, we
propose the Reliability-oriented Depth Refinement (RDR) module to estimate
dense depth with the fine-grained structure based on the fused features and
masks. We evaluate the effectiveness of our method on the synthetic and
real-world datasets, which shows that, even without pretraining, our method
outperforms the prior methods, e.g., RAMNet, especially in night scenes. Our
project homepage: https://vlislab22.github.io/SRFNet
FMapping: Factorized Efficient Neural Field Mapping for Real-Time Dense RGB SLAM
In this paper, we introduce FMapping, an efficient neural field mapping
framework that facilitates the continuous estimation of a colorized point cloud
map in real-time dense RGB SLAM. To achieve this challenging goal without
depth, a hurdle is how to improve efficiency and reduce the mapping uncertainty
of the RGB SLAM system. To this end, we first build up a theoretical analysis
by decomposing the SLAM system into tracking and mapping parts, and the mapping
uncertainty is explicitly defined within the frame of neural representations.
Based on the analysis, we then propose an effective factorization scheme for
scene representation and introduce a sliding window strategy to reduce the
uncertainty for scene reconstruction. Specifically, we leverage the factorized
neural field to decompose uncertainty into a lower-dimensional space, which
enhances robustness to noise and improves training efficiency. We then propose
the sliding window sampler to reduce uncertainty by incorporating coherent
geometric cues from observed frames during map initialization to enhance
convergence. Our factorized neural mapping approach enjoys some advantages,
such as low memory consumption, more efficient computation, and fast
convergence during map initialization. Experiments on two benchmark datasets
show that our method can update the map of high-fidelity colorized point clouds
around 2 seconds in real time while requiring no customized CUDA kernels.
Additionally, it utilizes x20 fewer parameters than the most concise neural
implicit mapping of prior methods for SLAM, e.g., iMAP [ 31] and around x1000
fewer parameters than the state-of-the-art approach, e.g., NICE-SLAM [ 42]. For
more details, please refer to our project homepage:
https://vlis2022.github.io/fmap/
Design of exponential state estimators for neural networks with mixed time delays
This is the post print version of the article. The official published version can be obtained from the link below - Copyright 2007 Elsevier Ltd.In this Letter, the state estimation problem is dealt with for a class of recurrent neural networks (RNNs) with mixed discrete and distributed delays. The activation functions are assumed to be neither monotonic, nor differentiable, nor bounded. We aim at designing a state estimator to estimate the neuron states, through available output measurements, such that the dynamics of the estimation error is globally exponentially stable in the presence of mixed time delays. By using the LaypunovāKrasovskii functional, a linear matrix inequality (LMI) approach is developed to establish sufficient conditions to guarantee the existence of the state estimators. We show that both the existence conditions and the explicit expression of the desired estimator can be characterized in terms of the solution to an LMI. A simulation example is exploited to show the usefulness of the derived LMI-based stability conditions.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the Nuffield Foundation of the UK under Grant NAL/00630/G, the Alexander von Humboldt Foundation of Germany, the Natural Science Foundation of Jiangsu Education Committee of China under Grants 05KJB110154 and BK2006064, and the National Natural Science Foundation of China under Grants 10471119 and 10671172
Robust stability for stochastic Hopfield neural networks with time delays
This is the post print version of the article. The official published version can be obtained from the link below - Copyright 2006 Elsevier Ltd.In this paper, the asymptotic stability analysis problem is considered for a class of uncertain stochastic neural networks with time delays and parameter uncertainties. The delays are time-invariant, and the uncertainties are norm-bounded that enter into all the network parameters. The aim of this paper is to establish easily verifiable conditions under which the delayed neural network is robustly asymptotically stable in the mean square for all admissible parameter uncertainties. By employing a LyapunovāKrasovskii functional and conducting the stochastic analysis, a linear matrix inequality (LMI) approach is developed to derive the stability criteria. The proposed criteria can be checked readily by using some standard numerical packages, and no tuning of parameters is required. Examples are provided to demonstrate the effectiveness and applicability of the proposed criteria.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the Nuffield Foundation of the UK under Grant NAL/00630/G, and the Alexander von Humboldt Foundation of German
OmniZoomer: Learning to Move and Zoom in on Sphere at High-Resolution
Omnidirectional images (ODIs) have become increasingly popular, as their
large field-of-view (FoV) can offer viewers the chance to freely choose the
view directions in immersive environments such as virtual reality. The M\"obius
transformation is typically employed to further provide the opportunity for
movement and zoom on ODIs, but applying it to the image level often results in
blurry effect and aliasing problem. In this paper, we propose a novel deep
learning-based approach, called \textbf{OmniZoomer}, to incorporate the
M\"obius transformation into the network for movement and zoom on ODIs. By
learning various transformed feature maps under different conditions, the
network is enhanced to handle the increasing edge curvatures, which alleviates
the blurry effect. Moreover, to address the aliasing problem, we propose two
key components. Firstly, to compensate for the lack of pixels for describing
curves, we enhance the feature maps in the high-resolution (HR) space and
calculate the transformed index map with a spatial index generation module.
Secondly, considering that ODIs are inherently represented in the spherical
space, we propose a spherical resampling module that combines the index map and
HR feature maps to transform the feature maps for better spherical correlation.
The transformed feature maps are decoded to output a zoomed ODI. Experiments
show that our method can produce HR and high-quality ODIs with the flexibility
to move and zoom in to the object of interest. Project page is available at
http://vlislab22.github.io/OmniZoomer/.Comment: Accepted by ICCV 202
Global exponential stability of generalized recurrent neural networks with discrete and distributed delays
This is the post print version of the article. The official published version can be obtained from the link below - Copyright 2006 Elsevier Ltd.This paper is concerned with analysis problem for the global exponential stability of a class of recurrent neural networks (RNNs) with mixed discrete and distributed delays. We first prove the existence and uniqueness of the equilibrium point under mild conditions, assuming neither differentiability nor strict monotonicity for the activation function. Then, by employing a new LyapunovāKrasovskii functional, a linear matrix inequality (LMI) approach is developed to establish sufficient conditions for the RNNs to be globally exponentially stable. Therefore, the global exponential stability of the delayed RNNs can be easily checked by utilizing the numerically efficient Matlab LMI toolbox, and no tuning of parameters is required. A simulation example is exploited to show the usefulness of the derived LMI-based stability conditions.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the Nuffield Foundation of the UK under Grant NAL/00630/G, and the Alexander von Humboldt Foundation of Germany
State estimation for discrete-time Markovian jumping neural networks with mixed mode-dependent delays
This is the post print version of the article. The official published version can be obtained from the link - Copyright 2008 Elsevier LtdIn this Letter, we investigate the state estimation problem for a new class of discrete-time neural networks with Markovian jumping parameters as well as mode-dependent mixed time-delays. The parameters of the discrete-time neural networks are subject to the switching from one mode to another at different times according to a Markov chain, and the mixed time-delays consist of both discrete and distributed delays that are dependent on the Markovian jumping mode. New techniques are developed to deal with the mixed time-delays in the discrete-time setting, and a novel LyapunovāKrasovskii functional is put forward to reflect the mode-dependent time-delays. Sufficient conditions are established in terms of linear matrix inequalities (LMIs) that guarantee the existence of the state estimators. We show that both the existence conditions and the explicit expression of the desired estimator can be characterized in terms of the solution to an LMI. A numerical example is exploited to show the usefulness of the derived LMI-based conditions.This work was supported in part by the Biotechnology and Biological Sciences Research Council (BBSRC) of the UK under Grants BB/C506264/1 and 100/EGM17735, the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grants GR/S27658/01 and EP/C524586/1, an International Joint Project sponsored by the Royal Society of the UK, the Natural Science Foundation of Jiangsu Province of China under Grant BK2007075, the National Natural Science Foundation of China under Grant 60774073, and the Alexander von Humboldt Foundation of Germany
Stochastic stability of uncertain Hopfield neural networks with discrete and distributed delays
This is the post print version of the article. The official published version can be obtained from the link below - Copyright 2006 Elsevier Ltd.This Letter is concerned with the global asymptotic stability analysis problem for a class of uncertain stochastic Hopfield neural networks with discrete and distributed time-delays. By utilizing a LyapunovāKrasovskii functional, using the well-known S-procedure and conducting stochastic analysis, we show that the addressed neural networks are robustly, globally, asymptotically stable if a convex optimization problem is feasible. Then, the stability criteria are derived in terms of linear matrix inequalities (LMIs), which can be effectively solved by some standard numerical packages. The main results are also extended to the multiple time-delay case. Two numerical examples are given to demonstrate the usefulness of the proposed global stability condition.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the Nuffield Foundation of the UK under Grant NAL/00630/G, and the Alexander von Humboldt Foundation of Germany
Exponential stability of delayed recurrent neural networks with Markovian jumping parameters
This is the post print version of the article. The official published version can be obtained from the link below - Copyright 2006 Elsevier Ltd.In this Letter, the global exponential stability analysis problem is considered for a class of recurrent neural networks (RNNs) with time delays and Markovian jumping parameters. The jumping parameters considered here are generated from a continuous-time discrete-state homogeneous Markov process, which are governed by a Markov process with discrete and finite state space. The purpose of the problem addressed is to derive some easy-to-test conditions such that the dynamics of the neural network is stochastically exponentially stable in the mean square, independent of the time delay. By employing a new LyapunovāKrasovskii functional, a linear matrix inequality (LMI) approach is developed to establish the desired sufficient conditions, and therefore the global exponential stability in the mean square for the delayed RNNs can be easily checked by utilizing the numerically efficient Matlab LMI toolbox, and no tuning of parameters is required. A numerical example is exploited to show the usefulness of the derived LMI-based stability conditions.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the Nuffield Foundation of the UK under Grant NAL/00630/G, and the Alexander von Humboldt Foundation of Germany
Discrete-time recurrent neural networks with time-varying delays: Exponential stability analysis
This is the post print version of the article. The official published version can be obtained from the link below - Copyright 2007 Elsevier LtdThis Letter is concerned with the analysis problem of exponential stability for a class of discrete-time recurrent neural networks (DRNNs) with time delays. The delay is of the time-varying nature, and the activation functions are assumed to be neither differentiable nor strict monotonic. Furthermore, the description of the activation functions is more general than the recently commonly used Lipschitz conditions. Under such mild conditions, we first prove the existence of the equilibrium point. Then, by employing a LyapunovāKrasovskii functional, a unified linear matrix inequality (LMI) approach is developed to establish sufficient conditions for the DRNNs to be globally exponentially stable. It is shown that the delayed DRNNs are globally exponentially stable if a certain LMI is solvable, where the feasibility of such an LMI can be easily checked by using the numerically efficient Matlab LMI Toolbox. A simulation example is presented to show the usefulness of the derived LMI-based stability condition.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the Nuffield Foundation of the UK under Grant NAL/00630/G, the Alexander von Humboldt Foundation of Germany, the Natural Science Foundation of Jiangsu Education Committee of China (05KJB110154), the NSF of Jiangsu Province of China (BK2006064), and the National Natural Science Foundation of China (10471119)
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