GAL: Geometric Adversarial Loss for Single-View 3D-Object Reconstruction

Li Jiang, Shaoshuai Shi, Xiaojuan Qi, Jiaya Jia; The European Conference on Computer Vision (ECCV), 2018, pp. 802-816


In this paper, we present a framework for reconstructing a point-based 3D model of an object from a single view image. 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 to add 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.

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author = {Jiang, Li and Shi, Shaoshuai and Qi, Xiaojuan and Jia, Jiaya},
title = {GAL: Geometric Adversarial Loss for Single-View 3D-Object Reconstruction},
booktitle = {The European Conference on Computer Vision (ECCV)},
month = {September},
year = {2018}