1,132 research outputs found
SmartCanvas: Context-inferred Interpretation of Sketches for Preparatory Design Studies
In early or preparatory design stages, an architect or designer sketches out rough ideas, not only about the object or structure being considered, but its relation to its spatial context. This is an iterative process, where the sketches are not only the primary means for testing and refining ideas, but also for communicating among a design team and to clients. Hence, sketching is the preferred media for artists and designers during the early stages of design, albeit with a major drawback: sketches are 2D and effects such as view perturbations or object movement are not supported, thereby inhibiting the design process. We present an interactive system that allows for the creation of a 3D abstraction of a designed space, built primarily by sketching in 2D within the context of an anchoring design or photograph. The system is progressive in the sense that the interpretations are refined as the user continues sketching. As a key technical enabler, we reformulate the sketch interpretation process as a selection optimization from a set of context-generated canvas planes in order to retrieve a regular arrangement of planes. We demonstrate our system (available at http:/geometry.cs.ucl.ac.uk/projects/2016/smartcanvas/) with a wide range of sketches and design studies
Generative Design in Minecraft (GDMC), Settlement Generation Competition
This paper introduces the settlement generation competition for Minecraft,
the first part of the Generative Design in Minecraft challenge. The settlement
generation competition is about creating Artificial Intelligence (AI) agents
that can produce functional, aesthetically appealing and believable settlements
adapted to a given Minecraft map - ideally at a level that can compete with
human created designs. The aim of the competition is to advance procedural
content generation for games, especially in overcoming the challenges of
adaptive and holistic PCG. The paper introduces the technical details of the
challenge, but mostly focuses on what challenges this competition provides and
why they are scientifically relevant.Comment: 10 pages, 5 figures, Part of the Foundations of Digital Games 2018
proceedings, as part of the workshop on Procedural Content Generatio
Consistent Two-Flow Network for Tele-Registration of Point Clouds
Rigid registration of partial observations is a fundamental problem in
various applied fields. In computer graphics, special attention has been given
to the registration between two partial point clouds generated by scanning
devices. State-of-the-art registration techniques still struggle when the
overlap region between the two point clouds is small, and completely fail if
there is no overlap between the scan pairs. In this paper, we present a
learning-based technique that alleviates this problem, and allows registration
between point clouds, presented in arbitrary poses, and having little or even
no overlap, a setting that has been referred to as tele-registration. Our
technique is based on a novel neural network design that learns a prior of a
class of shapes and can complete a partial shape. The key idea is combining the
registration and completion tasks in a way that reinforces each other. In
particular, we simultaneously train the registration network and completion
network using two coupled flows, one that register-and-complete, and one that
complete-and-register, and encourage the two flows to produce a consistent
result. We show that, compared with each separate flow, this two-flow training
leads to robust and reliable tele-registration, and hence to a better point
cloud prediction that completes the registered scans. It is also worth
mentioning that each of the components in our neural network outperforms
state-of-the-art methods in both completion and registration. We further
analyze our network with several ablation studies and demonstrate its
performance on a large number of partial point clouds, both synthetic and
real-world, that have only small or no overlap.Comment: Accepted to TVCG 2021, project page at
https://vcc.tech/research/2021/CTFNe
Simulating liquids on dynamically warping grids
We introduce dynamically warping grids for adaptive liquid simulation. Our primary contributions are a strategy for dynamically deforming regular grids over the course of a simulation and a method for efficiently utilizing these deforming grids for liquid simulation. Prior work has shown that unstructured grids are very effective for adaptive fluid simulations. However, unstructured grids often lead to complicated implementations and a poor cache hit rate due to inconsistent memory access. Regular grids, on the other hand, provide a fast, fixed memory access pattern and straightforward implementation. Our method combines the advantages of both: we leverage the simplicity of regular grids while still achieving practical and controllable spatial adaptivity. We demonstrate that our method enables adaptive simulations that are fast, flexible, and robust to null-space issues. At the same time, our method is simple to implement and takes advantage of existing highly-tuned algorithms
k-d Darts: Sampling by k-Dimensional Flat Searches
We formalize the notion of sampling a function using k-d darts. A k-d dart is
a set of independent, mutually orthogonal, k-dimensional subspaces called k-d
flats. Each dart has d choose k flats, aligned with the coordinate axes for
efficiency. We show that k-d darts are useful for exploring a function's
properties, such as estimating its integral, or finding an exemplar above a
threshold. We describe a recipe for converting an algorithm from point sampling
to k-d dart sampling, assuming the function can be evaluated along a k-d flat.
We demonstrate that k-d darts are more efficient than point-wise samples in
high dimensions, depending on the characteristics of the sampling domain: e.g.
the subregion of interest has small volume and evaluating the function along a
flat is not too expensive. We present three concrete applications using line
darts (1-d darts): relaxed maximal Poisson-disk sampling, high-quality
rasterization of depth-of-field blur, and estimation of the probability of
failure from a response surface for uncertainty quantification. In these
applications, line darts achieve the same fidelity output as point darts in
less time. We also demonstrate the accuracy of higher dimensional darts for a
volume estimation problem. For Poisson-disk sampling, we use significantly less
memory, enabling the generation of larger point clouds in higher dimensions.Comment: 19 pages 16 figure
A Deeper Look into DeepCap
Human performance capture is a highly important computer vision problem with many applications in movie production and virtual/augmented reality. Many previous performance capture approaches either required expensive multi-view setups or did not recover dense space-time coherent geometry with frame-to-frame correspondences. We propose a novel deep learning approach for monocular dense human performance capture. Our method is trained in a weakly supervised manner based on multi-view supervision completely removing the need for training data with 3D ground truth annotations. The network architecture is based on two separate networks that disentangle the task into a pose estimation and a non-rigid surface deformation step. Extensive qualitative and quantitative evaluations show that our approach outperforms the state of the art in terms of quality and robustness. This work is an extended version of DeepCap where we provide more detailed explanations, comparisons and results as well as applications
Bayesian Optimization with Unknown Constraints
Recent work on Bayesian optimization has shown its effectiveness in global
optimization of difficult black-box objective functions. Many real-world
optimization problems of interest also have constraints which are unknown a
priori. In this paper, we study Bayesian optimization for constrained problems
in the general case that noise may be present in the constraint functions, and
the objective and constraints may be evaluated independently. We provide
motivating practical examples, and present a general framework to solve such
problems. We demonstrate the effectiveness of our approach on optimizing the
performance of online latent Dirichlet allocation subject to topic sparsity
constraints, tuning a neural network given test-time memory constraints, and
optimizing Hamiltonian Monte Carlo to achieve maximal effectiveness in a fixed
time, subject to passing standard convergence diagnostics.Comment: 14 pages, 3 figure
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