WT
W.E.P. Tolsma
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1
Making it Clear
Using Vision Transformers in Multi-View Stereo on Specular and Transparent Materials
Transparency and specularity are challenging phenomena that modern depth perception systems have to deal with in order to be used in practice. A promising family of depth estimation methods is Multi-View Stereo (MVS), which combines multiple RGB images to predict depth, thus circumventing the need for costly specialized hardware. Although promising, finding pixel-to-pixel mappings between images is a challenging task, clouded by ambiguity. In order to determine the current ability to deal with such ambiguity, we introduce ToteMVS: a multi-view, multi-material synthetic dataset with diffuse, specular and transparent objects. Recent works in computer vision have effectively replaced Convolutional Neural Networks (CNNs) with the emerging Vision Transformer (ViT) architecture, but it remains unclear whether ViTs outperform CNNs in handling reflective and transparent materials. In our study, we use ToteMVS to compare ViT- and CNN-based architectures on the ability to extract useful features for depth estimation on diffuse, specular, and transparent objects. Our results show that, in contrast with the current trend of using ViTs over CNNs, the ViT-based model does not have a special capability for dealing with these challenging materials in the context of MVS. Our evaluation data, including related code, can be found on our \href{https://github.com/pietertolsma/ToteMVS/}{GitHub}.
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Transparency and specularity are challenging phenomena that modern depth perception systems have to deal with in order to be used in practice. A promising family of depth estimation methods is Multi-View Stereo (MVS), which combines multiple RGB images to predict depth, thus circumventing the need for costly specialized hardware. Although promising, finding pixel-to-pixel mappings between images is a challenging task, clouded by ambiguity. In order to determine the current ability to deal with such ambiguity, we introduce ToteMVS: a multi-view, multi-material synthetic dataset with diffuse, specular and transparent objects. Recent works in computer vision have effectively replaced Convolutional Neural Networks (CNNs) with the emerging Vision Transformer (ViT) architecture, but it remains unclear whether ViTs outperform CNNs in handling reflective and transparent materials. In our study, we use ToteMVS to compare ViT- and CNN-based architectures on the ability to extract useful features for depth estimation on diffuse, specular, and transparent objects. Our results show that, in contrast with the current trend of using ViTs over CNNs, the ViT-based model does not have a special capability for dealing with these challenging materials in the context of MVS. Our evaluation data, including related code, can be found on our \href{https://github.com/pietertolsma/ToteMVS/}{GitHub}.
The Routing Protocol for Low-Power and Lossy Networks (RPL) has gained in popularity since the increased connectivity of everyday items to the Internet. One of the discovered attacks on RPL is the rank attack, which opens up possibilities for attackers to control traffic in the RPL network by spoofing their priority. Many solutions have been proposed to mitigate this attack over the past few years. There is no perfect solution yet, partly because the success of a mitigation is dependent on the network configuration in which it is implemented. In some network configurations,as this paper will show, common mitigation solutions are less effective. By selecting and analyzing four well-cited mitigation and detection solutions, the effectiveness of these proposals is reviewed when the network is configured to use nonlinear objective functions (NOFs). After this, a proposal is given to defend against a rank attack when using NOFs. TRAIL was proposed as a solution for preventing decreased rank attacks and uses a challenge-response mechanism to verify the path from a node to the root. This paper proposes T-TRAIL; an extended version of TRAIL that allows the measurement of downwards-trip-time to detect outliers in the network. By doing this, the rank attack can be prevented when the network uses a NOF. Finally, an estimation of the performance impact of T-TRAIL on the network is given based on the performance measurements of TRAIL.
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The Routing Protocol for Low-Power and Lossy Networks (RPL) has gained in popularity since the increased connectivity of everyday items to the Internet. One of the discovered attacks on RPL is the rank attack, which opens up possibilities for attackers to control traffic in the RPL network by spoofing their priority. Many solutions have been proposed to mitigate this attack over the past few years. There is no perfect solution yet, partly because the success of a mitigation is dependent on the network configuration in which it is implemented. In some network configurations,as this paper will show, common mitigation solutions are less effective. By selecting and analyzing four well-cited mitigation and detection solutions, the effectiveness of these proposals is reviewed when the network is configured to use nonlinear objective functions (NOFs). After this, a proposal is given to defend against a rank attack when using NOFs. TRAIL was proposed as a solution for preventing decreased rank attacks and uses a challenge-response mechanism to verify the path from a node to the root. This paper proposes T-TRAIL; an extended version of TRAIL that allows the measurement of downwards-trip-time to detect outliers in the network. By doing this, the rank attack can be prevented when the network uses a NOF. Finally, an estimation of the performance impact of T-TRAIL on the network is given based on the performance measurements of TRAIL.