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Good quality reconstruction and comprehension of a scene rely on 3Destimation methods. The 3D information was usually obtained from images bystereo-photogrammetry, but deep learning has recently provided us withexcellent results for monocular depth estimation. Building up a sufficientlylarge and rich training dataset to achieve these results requires onerousprocessing. In this paper, we address the problem of learning outdoor 3D pointcloud from monocular data using a sparse ground-truth dataset. We proposePix2Point, a deep learning-based approach for monocular 3D point cloudprediction, able to deal with complete and challenging outdoor scenes. Ourmethod relies on a 2D-3D hybrid neural network architecture, and a supervisedend-to-end minimisation of an optimal transport divergence between pointclouds. We show that, when trained on sparse point clouds, our simple promisingapproach achieves a better coverage of 3D outdoor scenes than efficientmonocular depth methods.

Pix2Point: Learning Outdoor 3D Using Sparse Point Clouds and Optimal Transport