Ladybird: Quasi-Monte Carlo Sampling for Deep Implicit Field Based 3D Reconstruction with Symmetry

• URL: http://arxiv.org/abs/2007.13393v1
• Date: Mon, 27 Jul 2020 09:17:00 GMT
• Title: Ladybird: Quasi-Monte Carlo Sampling for Deep Implicit Field Based 3D Reconstruction with Symmetry
• Authors: Yifan Xu, Tianqi Fan, Yi Yuan, Gurprit Singh
• Abstract summary: We propose a sampling scheme that theoretically encourages generalization and results in fast convergence for SGD-based optimization algorithms. Based on the reflective symmetry of an object, we propose a feature fusion method that alleviates issues due to self-occlusions. Our proposed system Ladybird is able to create high quality 3D object reconstructions from a single input image.
• Score: 12.511526058118143
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep implicit field regression methods are effective for 3D reconstruction from single-view images. However, the impact of different sampling patterns on the reconstruction quality is not well-understood. In this work, we first study the effect of point set discrepancy on the network training. Based on Farthest Point Sampling algorithm, we propose a sampling scheme that theoretically encourages better generalization performance, and results in fast convergence for SGD-based optimization algorithms. Secondly, based on the reflective symmetry of an object, we propose a feature fusion method that alleviates issues due to self-occlusions which makes it difficult to utilize local image features. Our proposed system Ladybird is able to create high quality 3D object reconstructions from a single input image. We evaluate Ladybird on a large scale 3D dataset (ShapeNet) demonstrating highly competitive results in terms of Chamfer distance, Earth Mover's distance and Intersection Over Union (IoU).

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