Marcela Carvalho E. - Academia.edu (original) (raw)
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Graduate Center of the City University of New York
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Papers by Marcela Carvalho E.
2018 25th IEEE International Conference on Image Processing (ICIP), 2018
Depth estimation from a single monocular image has reached great performances thanks to recent wo... more Depth estimation from a single monocular image has reached great performances thanks to recent works based on deep networks. However, as various choices of losses, architectures and experimental conditions are proposed in the literature, it is difficult to establish their respective influence on the performances. In this paper we propose an in-depth study of various losses and experimental conditions for depth regression, on NYUv2 dataset. From this study we propose a new network for depth estimation combining an encoder-decoder architecture with an adversarial loss. This network reaches top ones state of the art on NUYv2 dataset while being simpler to train in a single phase.
2018 25th IEEE International Conference on Image Processing (ICIP), 2018
Depth estimation from a single monocular image has reached great performances thanks to recent wo... more Depth estimation from a single monocular image has reached great performances thanks to recent works based on deep networks. However, as various choices of losses, architectures and experimental conditions are proposed in the literature, it is difficult to establish their respective influence on the performances. In this paper we propose an in-depth study of various losses and experimental conditions for depth regression, on NYUv2 dataset. From this study we propose a new network for depth estimation combining an encoder-decoder architecture with an adversarial loss. This network reaches top ones state of the art on NUYv2 dataset while being simpler to train in a single phase.