@article{14320,
  abstract     = {The development of two-dimensional materials has resulted in a diverse range of novel, high-quality compounds with increasing complexity. A key requirement for a comprehensive quantitative theory is the accurate determination of these materials' band structure parameters. However, this task is challenging due to the intricate band structures and the indirect nature of experimental probes. In this work, we introduce a general framework to derive band structure parameters from experimental data using deep neural networks. We applied our method to the penetration field capacitance measurement of trilayer graphene, an effective probe of its density of states. First, we demonstrate that a trained deep network gives accurate predictions for the penetration field capacitance as a function of tight-binding parameters. Next, we use the fast and accurate predictions from the trained network to automatically determine tight-binding parameters directly from experimental data, with extracted parameters being in a good agreement with values in the literature. We conclude by discussing potential applications of our method to other materials and experimental techniques beyond penetration field capacitance.},
  author       = {Henderson, Paul M and Ghazaryan, Areg and Zibrov, Alexander A. and Young, Andrea F. and Serbyn, Maksym},
  issn         = {2469-9969},
  journal      = {Physical Review B},
  number       = {12},
  publisher    = {American Physical Society},
  title        = {{Deep learning extraction of band structure parameters from density of states: A case study on trilayer graphene}},
  doi          = {10.1103/physrevb.108.125411},
  volume       = {108},
  year         = {2023},
}

@article{8562,
  abstract     = {Cold bent glass is a promising and cost-efficient method for realizing doubly curved glass facades. They are produced by attaching planar glass sheets to curved frames and require keeping the occurring stress within safe limits.
However, it is very challenging to navigate the design space of cold bent glass panels due to the fragility of the material, which impedes the form-finding for practically feasible and aesthetically pleasing cold bent glass facades. We propose an interactive, data-driven approach for designing cold bent glass facades that can be seamlessly integrated into a typical architectural design pipeline. Our method allows non-expert users to interactively edit a parametric surface while providing real-time feedback on the deformed shape and maximum stress of cold bent glass panels. Designs are automatically refined to minimize several fairness criteria while maximal stresses are kept within glass limits. We achieve interactive frame rates by using a differentiable Mixture Density Network trained from more than a million simulations. Given a curved boundary, our regression model is capable of handling multistable
configurations and accurately predicting the equilibrium shape of the panel and its corresponding maximal stress. We show predictions are highly accurate and validate our results with a physical realization of a cold bent glass surface.},
  author       = {Gavriil, Konstantinos and Guseinov, Ruslan and Perez Rodriguez, Jesus and Pellis, Davide and Henderson, Paul M and Rist, Florian and Pottmann, Helmut and Bickel, Bernd},
  issn         = {1557-7368},
  journal      = {ACM Transactions on Graphics},
  number       = {6},
  publisher    = {Association for Computing Machinery},
  title        = {{Computational design of cold bent glass façades}},
  doi          = {10.1145/3414685.3417843},
  volume       = {39},
  year         = {2020},
}

@article{6952,
  abstract     = {We present a unified framework tackling two problems: class-specific 3D reconstruction from a single image, and generation of new 3D shape samples. These tasks have received considerable attention recently; however, most existing approaches rely on 3D supervision, annotation of 2D images with keypoints or poses, and/or training with multiple views of each object instance. Our framework is very general: it can be trained in similar settings to existing approaches, while also supporting weaker supervision. Importantly, it can be trained purely from 2D images, without pose annotations, and with only a single view per instance. We employ meshes as an output representation, instead of voxels used in most prior work. This allows us to reason over lighting parameters and exploit shading information during training, which previous 2D-supervised methods cannot. Thus, our method can learn to generate and reconstruct concave object classes. We evaluate our approach in various settings, showing that: (i) it learns to disentangle shape from pose and lighting; (ii) using shading in the loss improves performance compared to just silhouettes; (iii) when using a standard single white light, our model outperforms state-of-the-art 2D-supervised methods, both with and without pose supervision, thanks to exploiting shading cues; (iv) performance improves further when using multiple coloured lights, even approaching that of state-of-the-art 3D-supervised methods; (v) shapes produced by our model capture smooth surfaces and fine details better than voxel-based approaches; and (vi) our approach supports concave classes such as bathtubs and sofas, which methods based on silhouettes cannot learn.},
  author       = {Henderson, Paul M and Ferrari, Vittorio},
  issn         = {1573-1405},
  journal      = {International Journal of Computer Vision},
  pages        = {835--854},
  publisher    = {Springer Nature},
  title        = {{Learning single-image 3D reconstruction by generative modelling of shape, pose and shading}},
  doi          = {10.1007/s11263-019-01219-8},
  volume       = {128},
  year         = {2020},
}

@unpublished{8063,
  abstract     = {We present a generative model of images that explicitly reasons over the set
of objects they show. Our model learns a structured latent representation that
separates objects from each other and from the background; unlike prior works,
it explicitly represents the 2D position and depth of each object, as well as
an embedding of its segmentation mask and appearance. The model can be trained
from images alone in a purely unsupervised fashion without the need for object
masks or depth information. Moreover, it always generates complete objects,
even though a significant fraction of training images contain occlusions.
Finally, we show that our model can infer decompositions of novel images into
their constituent objects, including accurate prediction of depth ordering and
segmentation of occluded parts.},
  author       = {Anciukevicius, Titas and Lampert, Christoph and Henderson, Paul M},
  booktitle    = {arXiv},
  title        = {{Object-centric image generation with factored depths, locations, and appearances}},
  year         = {2020},
}

@inproceedings{8186,
  abstract     = {Numerous methods have been proposed for probabilistic generative modelling of
3D objects. However, none of these is able to produce textured objects, which
renders them of limited use for practical tasks. In this work, we present the
first generative model of textured 3D meshes. Training such a model would
traditionally require a large dataset of textured meshes, but unfortunately,
existing datasets of meshes lack detailed textures. We instead propose a new
training methodology that allows learning from collections of 2D images without
any 3D information. To do so, we train our model to explain a distribution of
images by modelling each image as a 3D foreground object placed in front of a
2D background. Thus, it learns to generate meshes that when rendered, produce
images similar to those in its training set.
  A well-known problem when generating meshes with deep networks is the
emergence of self-intersections, which are problematic for many use-cases. As a
second contribution we therefore introduce a new generation process for 3D
meshes that guarantees no self-intersections arise, based on the physical
intuition that faces should push one another out of the way as they move.
  We conduct extensive experiments on our approach, reporting quantitative and
qualitative results on both synthetic data and natural images. These show our
method successfully learns to generate plausible and diverse textured 3D
samples for five challenging object classes.},
  author       = {Henderson, Paul M and Tsiminaki, Vagia and Lampert, Christoph},
  booktitle    = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  issn         = {2575-7075},
  location     = {Virtual},
  pages        = {7498--7507},
  publisher    = {IEEE},
  title        = {{Leveraging 2D data to learn textured 3D mesh generation}},
  doi          = {10.1109/CVPR42600.2020.00752},
  year         = {2020},
}

@inproceedings{8188,
  abstract     = {A natural approach to generative modeling of videos is to represent them as a composition of moving objects. Recent works model a set of 2D sprites over a slowly-varying background, but without considering the underlying 3D scene that
gives rise to them. We instead propose to model a video as the view seen while moving through a scene with multiple 3D objects and a 3D background. Our model is trained from monocular videos without any supervision, yet learns to
generate coherent 3D scenes containing several moving objects. We conduct detailed experiments on two datasets, going beyond the visual complexity supported by state-of-the-art generative approaches. We evaluate our method on
depth-prediction and 3D object detection---tasks which cannot be addressed by those earlier works---and show it out-performs them even on 2D instance segmentation and tracking.},
  author       = {Henderson, Paul M and Lampert, Christoph},
  booktitle    = {34th Conference on Neural Information Processing Systems},
  isbn         = {9781713829546},
  location     = {Vancouver, Canada},
  pages        = {3106–3117},
  publisher    = {Curran Associates},
  title        = {{Unsupervised object-centric video generation and decomposition in 3D}},
  volume       = {33},
  year         = {2020},
}