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- W2895596173 abstract "In this paper, we present a framework for reconstructing a point-based 3D model of an object from a single-view image. We found distance metrics, like Chamfer distance, were used in previous work to measure the difference of two point sets and serve as the loss function in point-based reconstruction. However, such point-point loss does not constrain the 3D model from a global perspective. We propose adding geometric adversarial loss (GAL). It is composed of two terms where the geometric loss ensures consistent shape of reconstructed 3D models close to ground-truth from different viewpoints, and the conditional adversarial loss generates a semantically-meaningful point cloud. GAL benefits predicting the obscured part of objects and maintaining geometric structure of the predicted 3D model. Both the qualitative results and quantitative analysis manifest the generality and suitability of our method." @default.
- W2895596173 created "2018-10-12" @default.
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- W2895596173 date "2018-01-01" @default.
- W2895596173 modified "2023-10-16" @default.
- W2895596173 title "GAL: Geometric Adversarial Loss for Single-View 3D-Object Reconstruction" @default.
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- W2895596173 doi "https://doi.org/10.1007/978-3-030-01237-3_49" @default.
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