Digging Into Self-Supervised Monocular Depth Estimation

@google.com arXiv:1609.03499v2 [cs.SD] 19 Sep 2016

where 1 <x t <1 and = 255. This non-linear quantization produces a significantly better reconstruction than a simple linear quantization scheme. …

arXiv:0706.3639v1 [cs.AI] 25 Jun 2007

arXiv:0706.3639v1 [cs.AI] 25 Jun 2007 Technical Report IDSIA-07-07 A Collection of Definitions of Intelligence Shane Legg IDSIA, Galleria …

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Deep Residual Learning for Image Recognition Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun Microsoft Research fkahe, v-xiangz, v-shren, jiansung@microsoft.com

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arXiv:1301.3781v3 [cs.CL] 7 Sep 2013

For all the following models, the training complexity is proportional to O = E T Q; (1) where E is number of the training epochs, T is the number of …

A Tutorial on UAVs for Wireless Networks: …

A Tutorial on UAVs for Wireless Networks: Applications, Challenges, and Open Problems Mohammad Mozaffari 1, ... to UAVs in wireless communications is the work in …

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Adversarial Generative Nets: Neural Network …

Adversarial Generative Nets: Neural Network Attacks on State-of-the-Art Face Recognition Mahmood Sharif, Sruti Bhagavatula, Lujo Bauer Carnegie Mellon University

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Mastering Chess and Shogi by Self-Play with a …

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Going deeper with convolutions - arXiv

Going deeper with convolutions Christian Szegedy Google Inc. Wei Liu University of North Carolina, Chapel Hill Yangqing Jia Google Inc. Pierre Sermanet

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3D Reconstruction from Multiple Images

3.4 Multi View Stereo The Multi View Stereo algorithms are used to generate a dense 3D reconstruction of the object or scene. The techniques are usually based on the measurement of a consistency function, a function to measure whether \this 3D model is …

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KinectFusion: Real-Time Dense Surface Mapping and …

and multi-view stereo (MVS) has produced many compelling re-sults, in particular accurate camera tracking and sparse reconstruc-tions (e.g. [10]), and increasingly reconstruction of dense surfaces (e.g. [24]). However, much of this work was not motivated by real-time applications. Research on simultaneous localisation and mapping (SLAM) has

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Digging Into Self-Supervised Monocular Depth Estimation

the novel view given the relative pose between those two views. Classical binocular and multi-view stereo methods typically address this ambiguity by enforcing smoothness in the depth maps, and by computing photo-consistency on patches when solving for per-pixel depth via global opti-mization e.g. [11].

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