SIGGRAPH Asia 2018 Papers

Holographic displays have great potential to realize mixed reality by modulating the wavefront of light in a fundamental manner. As a computational display, holographic displays offer a large degree of freedom, such as focus cue generation and vision correction. However, the limited bandwidth of spatial light modulator imposes an inherent trade-off relationship between the field of view and eye-box size. Thus, we demonstrate the first practical eye-box expansion method for a holographic near-eye display. Instead of providing an intrinsic large exit-pupil, we shift the optical system's exit-pupil to cover the expanded eye-box area with pupil-tracking. For compact implementation, a pupil-shifting holographic optical element (PSHOE) is proposed that can reduce the form factor for exit-pupil shifting. A thorough analysis of the design parameters and display performance are provided. In particular, we provide a comprehensive analysis of the incorporation of the holographic optical element into a holographic display system. The influence of holographic optical elements on the intrinsic exit-pupil and pupil switching is revealed by numerical simulation and Wigner distribution function analysis.

We introduce a generative model for 3D man-made shapes. The presented method takes a global-to-local (G2L) approach. An adversarial network (GAN) is built first to construct the overall structure of the shape, segmented and labeled into parts. A novel conditional auto-encoder (AE) is then augmented to act as a part-level refiner. The GAN, associated with additional local discriminators and quality losses, synthesizes a voxel-based model, and assigns the voxels with part labels that are represented in separate channels. The AE is trained to amend the initial synthesis of the parts, yielding more plausible part geometries. We also introduce new means to measure and evaluate the performance of an adversarial generative model. We demonstrate that our global-to-local generative model produces significantly better results than a plain three-dimensional GAN, in terms of both their shape variety and the distribution with respect to the training data.

Markov chain Monte Carlo (MCMC) rendering utilizes a sequence of correlated path samples which is obtained by iteratively mutating the current state to the next. The efficiency of MCMC rendering depends on how well the mutation strategy is designed to adapt to the local structure of the state space. We present a novel MCMC rendering method that automatically adapts the step sizes of the mutations to the geometry of the rendered scene. Our geometry-aware path space perturbation largely avoids tentative samples with zero contribution due to occlusion. Our method limits the mutation step size by estimating the maximum opening angle of a cone, centered around a segment of a light transport path, where no geometry obstructs visibility. This geometry-aware mutation increases the acceptance rates, while not degrading the sampling quality. As this cone estimation introduces a considerable overhead if done naively, to make our approach efficient, we discuss and analyze fast approximate methods for cone angle estimation which utilize the acceleration structure already present for the ray-geometry intersection. Our new approach, integrated into the framework of Metropolis light transport, can achieve results with lower error and less artifact in equal time compared to current path space mutation techniques.

We propose a real-time method for the infrastructure-free estimation of articulated human motion. The approach leverages a swarm of camera-equipped flying robots and jointly optimizes the swarm's and skeletal states, which include the 3D joint positions and a set of bones. Our method allows to track the motion of human subjects, for example an athlete, over long time horizons and long distances, in challenging settings and at large scale, where fixed infrastructure approaches are not applicable. The proposed algorithm uses active infra-red markers, runs in real-time and accurately estimates robot and human pose parameters online without the need for accurately calibrated or stationary mounted cameras. Our method i) estimates a global coordinate frame for the MAV swarm, ii) jointly optimizes the human pose and relative camera positions, and iii) estimates the length of the human bones. The entire swarm is then controlled via a model predictive controller to maximize visibility of the subject from multiple viewpoints even under fast motion such as jumping or jogging. We demonstrate our method in a number of difficult scenarios including capture of long locomotion sequences at the scale of a triplex gym, in non-planar terrain, while climbing and in outdoor scenarios.

We propose FlexMaps, a novel framework for fabricating smooth shapes out of flat, flexible panels with tailored mechanical properties. We start by mapping the 3D surface onto a 2D domain as in traditional UV mapping to design a set of deformable flat panels called FlexMaps. For these panels, we design and obtain specific mechanical properties such that, once they are assembled, the static equilibrium configuration matches the desired 3D shape. FlexMaps can be fabricated from an almost rigid material, such as wood or plastic, and are made flexible in a controlled way by using computationally designed spiraling microstructures.

Current AR systems only track sparse geometric features but do not compute depth for all pixels. For this reason, most AR effects are pure overlays that can never be occluded by real objects. We present a novel algorithm that propagates sparse depth to every pixel in near realtime. The produced depth maps are spatio-temporally smooth but exhibit sharp discontinuities at depth edges. This enables AR effects that can fully interact with and be occluded by the real scene. Our algorithm uses a video and a sparse SLAM reconstruction as input. It starts by estimating soft depth edges from the gradient of optical flow fields. Because optical flow is unreliable near occlusions we compute forward and backward flow fields and fuse the resulting depth edges using a novel reliability measure. We then localize the depth edges by thinning and aligning them with image edges. Finally, we optimize the propagated depth smoothly but encourage discontinuities at the recovered depth edges. We present results for numerous real-world examples and demonstrate the effectiveness for several occlusion-aware AR video effects. To quantitatively evaluate our algorithm we characterize the properties that make depth maps desirable for AR applications, and present novel evaluation metrics that capture how well these are satisfied. Our results compare favorably to a set of competitive baseline algorithms in this context.

A common operation in geometry processing is solving symmetric and positive semi-definite systems on a subset of a mesh, with conditions for the vertices at the boundary of the region. This is commonly done by setting up the linear system for the sub-mesh, factorizing the system (potentially applying preordering to improve sparseness of the factors), and then solving by back-substitution. This approach suffers from a comparably high setup cost for each local operation. We propose to reuse factorizations defined on the full mesh to solve linear problems on sub-meshes. We show how an update on sparse matrices can be performed in a particularly efficient way to obtain the factorization of the operator on a sun-mesh significantly outperforming general factor updates and complete refactorization. We analyze the resulting speedup for a variety of situations and demonstrate that our method outperforms factorization of a new matrix by a factor of up to 10 while never being slower in our experiments.

We present a fully automatic method that finds a small number of machine fabricable wires with minimal overlap to reproduce a wire sculpture design as a 3D shape abstraction. Importantly, we consider non-planar wires, which can be fabricated by a wire bending machine, to enable efficient construction of complex 3D sculptures that cannot be achieved by previous works. We call our wires Eulerian wires , since they are as Eulerian as possible with small overlap to form the target design together. Finding such Eulerian wires is highly challenging, due to an enormous search space. After exploring a variety of optimization strategies, we formulate a population-based hybrid metaheuristic model, and design the join, bridge and split operators to refine the solution wire sets in the population. We start the exploration of each solution wire set in a bottom-up manner, and adopt an adaptive simulated annealing model to regulate the exploration. By further formulating a meta model on top to optimize the cooling schedule, and precomputing fabricable subwires, our method can efficiently find promising solutions with low wire count and overlap in one to two minutes. We demonstrate the efficiency of our method on a rich variety of wire sculptures, and physically fabricate several of them. Our results show clear improvements over other optimization alternatives in terms of solution quality, versatility, and scalability.

We introduce an extremely scalable and efficient yet simple palette-based image decomposition algorithm. Given an RGB image and set of palette colors, our algorithm decomposes the image into a set of additive mixing layers, each of which corresponds to a palette color applied with varying weight. Our approach is based on the geometry of images in RGBXY-space. This new geometric approach is orders of magnitude more efficient than previous work and requires no numerical optimization. We provide an implementation of the algorithm in 48 lines of Python code. We demonstrate a real-time layer decomposition tool in which users can interactively edit the palette to adjust the layers. After preprocessing, our algorithm can decompose 6 MP images into layers in 20 milliseconds.

We propose a technique for interpolating between probability distributions on discrete surfaces, based on the theory of optimal transport. Unlike previous attempts that use linear programming, our method is based on a dynamical formulation of quadratic optimal transport proposed for flat domains by Benamou and Brenier [2000], adapted to discrete surfaces. Our structure-preserving construction yields a Riemannian metric on the (finite-dimensional) space of probability distributions on a discrete surface, which translates the so-called Otto calculus to discrete language. From a practical perspective, our technique provides a smooth interpolation between distributions on discrete surfaces with less diffusion than state-of-the-art algorithms involving entropic regularization. Beyond interpolation, we show how our discrete notion of optimal transport extends to other tasks, such as distribution-valued Dirichlet problems and time integration of gradient flows.

Gradient-based methods are becoming increasingly important for computer graphics, machine learning, and computer vision. The ability to compute gradients is crucial to optimization, inverse problems, and deep learning. In rendering, the gradient is required with respect to variables such as camera parameters, light sources, scene geometry, or material appearance. However, computing the gradient of rendering is challenging because the rendering integral includes visibility terms that are not differentiable. Previous work on differentiable rendering has focused on approximate solutions. They often do not handle secondary effects such as shadows or global illumination, or they do not provide the gradient with respect to variables other than pixel coordinates. We introduce a general-purpose differentiable ray tracer, which, to our knowledge, is the first comprehensive solution that is able to compute derivatives of scalar functions over a rendered image with respect to arbitrary scene parameters such as camera pose, scene geometry, materials, and lighting parameters. The key to our method is a novel edge sampling algorithm that directly samples the Dirac delta functions introduced by the derivatives of the discontinuous integrand. We also develop efficient importance sampling methods based on spatial hierarchies. Our method can generate gradients in times running from seconds to minutes depending on scene complexity and desired precision. We interface our differentiable ray tracer with the deep learning library PyTorch and show prototype applications in inverse rendering and the generation of adversarial examples for neural networks.

Augmented reality (AR) for smartphones has matured from a technology for earlier adopters, available only on select high-end phones, to one that is truly available to the general public. One of the key breakthroughs has been in low-compute methods for six degree of freedom (6DoF) tracking on phones using only the existing hardware (camera and inertial sensors). 6DoF tracking is the cornerstone of smartphone AR allowing virtual content to be precisely locked on top of the real world. However, to really give users the impression of believable AR, one requires mobile depth. Without depth, even simple effects such as a virtual object being correctly occluded by the real-world is impossible. However, requiring a mobile depth sensor would severely restrict the access to such features. In this article, we provide a novel pipeline for mobile depth that supports a wide array of mobile phones, and uses only the existing monocular color sensor. Through several technical contributions, we provide the ability to compute low latency dense depth maps using only a single CPU core of a wide range of (medium-high) mobile phones. We demonstrate the capabilities of our approach on high-level AR applications including real-time navigation and shopping.

Delaunay meshes (DM) are a special type of manifold triangle meshes --- where the local Delaunay condition holds everywhere --- and find important applications in digital geometry processing. This paper addresses the general DM simplification problem: given an arbitrary manifold triangle mesh M with n vertices and the user-specified resolution m (< n ), compute a Delaunay mesh M * with m vertices that has the least Hausdorffdistance to M. To solve the problem, we abstract the simplification process using a 2D Cartesian grid model, in which each grid point corresponds to triangle meshes with a certain number of vertices and a simplification process is a monotonic path on the grid. We develop a novel differential-evolution-based method to compute a low-cost path, which leads to a high quality Delaunay mesh. Extensive evaluation shows that our method consistently outperforms the existing methods in terms of approximation error. In particular, our method is highly effective for small-scale CAD models and man-made objects with sharp features but less details. Moreover, our method is fully automatic and can preserve sharp features well and deal with models with multiple components, whereas the existing methods often fail.

We aim to generate high resolution shallow depth-of-field (DoF) images from a single all-in-focus image with controllable focal distance and aperture size. To achieve this, we propose a novel neural network model comprised of a depth prediction module, a lens blur module, and a guided upsampling module. All modules are differentiable and are learned from data. To train our depth prediction module, we collect a dataset of 2462 RGB-D images captured by mobile phones with a dual-lens camera, and use existing segmentation datasets to improve border prediction. We further leverage a synthetic dataset with known depth to supervise the lens blur and guided upsampling modules. The effectiveness of our system and training strategies are verified in the experiments. Our method can generate high-quality shallow DoF images at high resolution, and produces significantly fewer artifacts than the baselines and existing solutions for single image shallow DoF synthesis. Compared with the iPhone portrait mode, which is a state-of-the-art shallow DoF solution based on a dual-lens depth camera, our method generates comparable results, while allowing for greater flexibility to choose focal points and aperture size, and is not limited to one capture setup.

Addressing vergence-accommodation conflict in head-mounted displays (HMDs) requires resolving two interrelated problems. First, the hardware must support viewing sharp imagery over the full accommodation range of the user. Second, HMDs should accurately reproduce retinal defocus blur to correctly drive accommodation. A multitude of accommodation-supporting HMDs have been proposed, with three architectures receiving particular attention: varifocal, multifocal, and light field displays. These designs all extend depth of focus, but rely on computationally expensive rendering and optimization algorithms to reproduce accurate defocus blur (often limiting content complexity and interactive applications). To date, no unified framework has been proposed to support driving these emerging HMDs using commodity content. In this paper, we introduce DeepFocus , a generic, end-to-end convolutional neural network designed to efficiently solve the full range of computational tasks for accommodation-supporting HMDs. This network is demonstrated to accurately synthesize defocus blur, focal stacks, multilayer decompositions, and multiview imagery using only commonly available RGB-D images, enabling real-time, near-correct depictions of retinal blur with a broad set of accommodation-supporting HMDs.

In this paper, we present a novel unsupervised learning method for pixelization. Due to the difficulty in creating pixel art, preparing the paired training data for supervised learning is impractical. Instead, we propose an unsupervised learning framework to circumvent such difficulty. We leverage the dual nature of the pixelization and depixelization, and model these two tasks in the same network in a bi-directional manner with the input itself as training supervision. These two tasks are modeled as a cascaded network which consists of three stages for different purposes. GridNet transfers the input image into multi-scale grid-structured images with different aliasing effects. PixelNet associated with GridNet to synthesize pixel arts with sharp edges and perceptually optimal local structures. DepixelNet connects the previous network and aims to recover the pixelized result to the original image. For the sake of unsupervised learning, the mirror loss is proposed to hold the reversibility of feature representations in the process. In addition, adversarial, L1, and gradient losses are involved in the network to obtain pixel arts by retaining color correctness and smoothness. We show that our technique can synthesize crisper and perceptually more appropriate pixel arts than state-of-the-art image downscaling methods. We evaluate the proposed method with extensive experiments on many images. The proposed method outperforms state-of-the-art methods in terms of visual quality and user preference.

Object functionality is often expressed through part articulation - as when the two rigid parts of a scissor pivot against each other to perform the cutting function. Such articulations are often similar across objects within the same functional category. In this paper we explore how the observation of different articulation states provides evidence for part structure and motion of 3D objects. Our method takes as input a pair of unsegmented shapes representing two different articulation states of two functionally related objects, and induces their common parts along with their underlying rigid motion. This is a challenging setting, as we assume no prior shape structure, no prior shape category information, no consistent shape orientation, the articulation states may belong to objects of different geometry, plus we allow inputs to be noisy and partial scans, or point clouds lifted from RGB images. Our method learns a neural network architecture with three modules that respectively propose correspondences, estimate 3D deformation flows, and perform segmentation. To achieve optimal performance, our architecture alternates between correspondence, deformation flow, and segmentation prediction iteratively in an ICP-like fashion. Our results demonstrate that our method significantly outperforms state-of-the-art techniques in the task of discovering articulated parts of objects. In addition, our part induction is object-class agnostic and successfully generalizes to new and unseen objects.