L
Lars Mescheder
Researcher at Max Planck Society
Publications - 28
Citations - 7475
Lars Mescheder is an academic researcher from Max Planck Society. The author has contributed to research in topics: 3D reconstruction & Deep learning. The author has an hindex of 20, co-authored 28 publications receiving 4239 citations. Previous affiliations of Lars Mescheder include University of Tübingen.
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Occupancy Networks: Learning 3D Reconstruction in Function Space
TL;DR: This paper proposes Occupancy Networks, a new representation for learning-based 3D reconstruction methods that encodes a description of the 3D output at infinite resolution without excessive memory footprint, and validate that the representation can efficiently encode 3D structure and can be inferred from various kinds of input.
Proceedings ArticleDOI
Occupancy Networks: Learning 3D Reconstruction in Function Space
TL;DR: In this paper, the authors propose Occupancy Networks, which implicitly represent the 3D surface as the continuous decision boundary of a deep neural network classifier, which can be used for learning-based 3D reconstruction methods.
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Which Training Methods for GANs do actually Converge
TL;DR: This paper describes a simple yet prototypical counterexample showing that in the more realistic case of distributions that are not absolutely continuous, unregularized GAN training is not always convergent, and extends convergence results to more general GANs and proves local convergence for simplified gradient penalties even if the generator and data distribution lie on lower dimensional manifolds.
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Differentiable Volumetric Rendering: Learning Implicit 3D Representations Without 3D Supervision
TL;DR: This work proposes a differentiable rendering formulation for implicit shape and texture representations, showing that depth gradients can be derived analytically using the concept of implicit differentiation, and finds that this method can be used for multi-view 3D reconstruction, directly resulting in watertight meshes.
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Convolutional Occupancy Networks
TL;DR: Convolutional Occupancy Networks is proposed, a more flexible implicit representation for detailed reconstruction of objects and 3D scenes that enables the fine-grained implicit 3D reconstruction of single objects, scales to large indoor scenes, and generalizes well from synthetic to real data.