Imitating Popular Photos to Select Views for an Indoor Scene

Selecting informative and visually appealing views for 3D indoor scenes is beneficial for the housing, decoration, and entertainment industries. A set of views that exhibit comfort, aesthetics, and functionality of a particular scene can attract customers and facilitate business transactions. However, selecting views for an indoor scene is challenging because the system has to consider not only the need to reveal as much information as possible, but also object arrangements, occlusions, and characteristics. Since there can be many principles utilized to guide the view selection, and various principles to follow under different circumstances, we achieve the goal by imitating popular photos on the Internet. Specifically, we select the view that can optimize the contour similarity of corresponding objects to the photo. Because the selected view can be inadequate if object arrangements in the 3D scene and the photo are different, our system imitates many popular photos and selects a certain number of views. After that, it clusters the selected views and determines the view/cluster centers by the weighted average to finally exhibit the scene. Experimental results demonstrate that the views selected by our method are visually appealing.     

Efficient BVH‐based Collision Detection Scheme with Ordering and Restructuring

Bounding volume hierarchy (BVH) has been widely adopted as the acceleration structure in broad‐phase collision detection. Previous state‐of‐the‐art BVH‐based collision detection approaches exploited the spatio‐temporal coherence of simulations by maintaining a bounding volume test tree (BVTT) front. A major drawback of these algorithms is that large deformations in the scenes decrease culling efficiency and slow down collision queries. Moreover, for front‐based methods, the inefficient caching on GPU caused by the arbitrary layout of BVH and BVTT front nodes becomes a critical performance issue. We present a fast and robust BVH‐based collision detection scheme on GPU that addresses the above problems by ordering and restructuring BVHs and BVTT fronts. Our techniques are based on the use of histogram sort and an auxiliary structure BVTT front log, through which we analyze the dynamic status of BVTT front and BVH quality. Our approach efficiently handles inter‐ and intra‐object collisions and performs especially well in simulations where there is considerable spatio‐temporal coherence. The benchmark results demonstrate that our approach is significantly faster than the previous BVH‐based method, and also outperforms other state‐of‐the‐art spatial subdivision schemes in terms of speed.     

Fast Penetration Volume for Rigid Bodies

Handling collisions among a large number of bodies can be a performance bottleneck in video games and many other real‐time applications. We present a new framework for detecting and resolving collisions using the penetration volume as an interpenetration measure. Given two non‐convex polyhedral bodies, a new sampling paradigm locates their near‐contact configurations in advance, and stores associated contact information in a compact database. At runtime, we retrieve a given configuration's nearest neighbors. By taking advantage of the penetration volume's continuity, cheap geometric methods can use the neighbors to estimate contact information as well as a translational gradient. This results in an extremely fast, geometry‐independent, and trivially parallelizable computation, which constitutes the first global volume‐based collision resolution. When processing multiple collisions simultaneously on a 4‐core processor, the average running cost is as low as 5 μs. Furthermore, no additional proximity or contact‐regions queries are required. These results are orders of magnitude faster than previous penetration volume approaches.     

Saliency‐aware Real‐time Volumetric Fusion for Object Reconstruction

We present a real‐time approach for acquiring 3D objects with high fidelity using hand‐held consumer‐level RGB‐D scanning devices. Existing real‐time reconstruction methods typically do not take the point of interest into account, and thus might fail to produce clean reconstruction results of desired objects due to distracting objects or backgrounds. In addition, any changes in background during scanning, which can often occur in real scenarios, can easily break up the whole reconstruction process. To address these issues, we incorporate visual saliency into a traditional real‐time volumetric fusion pipeline. Salient regions detected from RGB‐D frames suggest user‐intended objects, and by understanding user intentions our approach can put more emphasis on important targets, and meanwhile, eliminate disturbance of non‐important objects. Experimental results on real‐world scans demonstrate that our system is capable of effectively acquiring geometric information of salient objects in cluttered real‐world scenes, even if the backgrounds are changing.     

Controllable Dendritic Crystal Simulation Using Orientation Field

Real world dendritic growths show charming structures by their exquisite balance between the symmetry and randomness in the crystal formation. Other than the variety in the natural crystals, richer visual appearance of crystals can benefit from artificially controlling of the crystal growth on its growing directions and shapes. In this paper, by introducing one extra dimension of freedom, i.e. the orientation field, into the simulation, we propose an efficient algorithm for dendritic crystal simulation that is able to reproduce arbitrary symmetry patterns with different levels of asymmetry breaking effect on general grids or meshes, including spreading on curved surfaces and growth in 3D. Flexible artistic control is also enabled in a unified manner by exploiting and guiding the orientation field in the visual simulation. We show the effectiveness of our approach by various demonstrations of simulation results.     

Sensor‐aware Normal Estimation for Point Clouds from 3D Range Scans

Normal vectors are essential for many point cloud operations, including segmentation, reconstruction and rendering. The robust estimation of normal vectors from 3D range scans is a challenging task due to undersampling and noise, specially when combining points sampled from multiple sensor locations. Our error model assumes a Gaussian distribution of the range error with spatially‐varying variances that depend on sensor distance and reflected intensity, mimicking the features of Lidar equipment. In this paper we study the impact of measurement errors on the covariance matrices of point neighborhoods. We show that covariance matrices of the true surface points can be estimated from those of the acquired points plus sensor‐dependent directional terms. We derive a lower bound on the neighbourhood size to guarantee that estimated matrix coefficients will be within a predefined error with a prescribed probability. This bound is key for achieving an optimal trade‐off between smoothness and fine detail preservation. We also propose and compare different strategies for handling neighborhoods with samples coming from multiple materials and sensors. We show analytically that our method provides better normal estimates than competing approaches in noise conditions similar to those found in Lidar equipment.     

Modeling Cumulus Cloud Scenes from High‐resolution Satellite Images

We present a reconstruction framework, which fits physically‐based constraints to model large‐scale cloud scenes from satellite images. Applications include weather phenomena visualization, flight simulation, and weather spotter training. In our method, the cloud shape is assumed to be composed of a cloud top surface and a nearly flat cloud base surface. Based on this, an effective method of multi‐spectral data processing is developed to obtain relevant information for calculating the cloud base height and the cloud top height, including ground temperature, cloud top temperature and cloud shadow. A lapse rate model is proposed to formulate cloud shape as an implicit function of temperature lapse rate and cloud base temperature. After obtaining initial cloud shapes, we enrich the shapes by a fractal method and represent reconstructed clouds by a particle system. Experiment results demonstrate the capability of our method in generating physically sound large‐scale cloud scenes from high‐resolution satellite images.     

Distant Collision Response in Rigid Body Simulations

We use a finite element model to predict the vibration response of objects in a rigid body simulation, such that rigid objects are augmented to provide a plausible elastic collision response between distant objects due to vibration. We start with a generalized eigenvalue decomposition of the elastic model to precompute a response to an impact at any point on an elastic object with fixed boundary conditions. Then, given a collision between objects, we generate an approximate response impulse to distribute to other objects already in contact with the colliding bodies. This can lead to distant impacts causing an object to slip, or a delicate stack of objects to fall. We also use a geodesic distance based spatial attenuation approximation for travelling waves in objects to respond to an impact at one contact with an impulse at other locations. This response ultimately allows a long distance relationship between contacts, both across a single object being struck, but also traversing the contact graph of a larger collection of objects. We qualitatively validate our approach with a ground truth simulation, and demonstrate a number of scenarios where a long distance relationship between contacts is valuable.     

Sequences with Low‐Discrepancy Blue‐Noise 2‐D Projections

Distributions of samples play a very important role in rendering, affecting variance, bias and aliasing in Monte‐Carlo and Quasi‐Monte Carlo evaluation of the rendering equation. In this paper, we propose an original sampler which inherits many important features of classical low‐discrepancy sequences (LDS): a high degree of uniformity of the achieved distribution of samples, computational efficiency and progressive sampling capability. At the same time, we purposely tailor our sampler in order to improve its spectral characteristics, which in turn play a crucial role in variance reduction, anti‐aliasing and improving visual appearance of rendering. Our sampler can efficiently generate sequences of multidimensional points, whose power spectra approach so‐called Blue‐Noise (BN) spectral property while preserving low discrepancy (LD) in certain 2‐D projections. In our tile‐based approach, we perform permutations on subsets of the original Sobol LDS. In a large space of all possible permutations, we select those which better approach the target BN property, using pair‐correlation statistics. We pre‐calculate such “good” permutations for each possible Sobol pattern, and store them in a lookup table efficiently accessible in runtime. We provide a complete and rigorous proof that such permutations preserve dyadic partitioning and thus the LDS properties of the point set in 2‐D projections. Our construction is computationally efficient, has a relatively low memory footprint and supports adaptive sampling. We validate our method by performing spectral/discrepancy/aliasing analysis of the achieved distributions, and provide variance analysis for several target integrands of theoretical and practical interest.     

Real‐time Locomotion Controller using an Inverted‐Pendulum‐based Abstract Model

In this paper, we propose a novel motion controller for the online generation of natural character locomotion that adapts to new situations such as changing user control or applying external forces. This controller continuously estimates the next footstep while walking and running, and automatically switches the stepping strategy based on situational changes. To develop the controller, we devise a new physical model called an inverted‐pendulum‐based abstract model (IPAM). The proposed abstract model represents high‐dimensional character motions, inheriting the naturalness of captured motions by estimating the appropriate footstep location, speed and switching time at every frame. The estimation is achieved by a deep learning based regressor that extracts important features in captured motions. To validate the proposed controller, we train the model using captured motions of a human stopping, walking, and running in a limited space. Then, the motion controller generates human‐like locomotion with continuously varying speeds, transitions between walking and running, and collision response strategies in a cluttered space in real time.     

Distributing Monte Carlo Errors as a Blue Noise in Screen Space by Permuting Pixel Seeds Between Frames

Recent work has shown that distributing Monte Carlo errors as a blue noise in screen space improves the perceptual quality of rendered images. However, obtaining such distributions remains an open problem with high sample counts and high‐dimensional rendering integrals. In this paper, we introduce a temporal algorithm that aims at overcoming these limitations. Our algorithm is applicable whenever multiple frames are rendered, typically for animated sequences or interactive applications. Our algorithm locally permutes the pixel sequences (represented by their seeds) to improve the error distribution across frames. Our approach works regardless of the sample count or the dimensionality and significantly improves the images in low‐varying screen‐space regions under coherent motion. Furthermore, it adds negligible overhead compared to the rendering times. Note: our supplemental material provides more results with interactive comparisons against previous work.     

Stochastic Light Culling for VPLs on GGX Microsurfaces

This paper introduces a real‐time rendering method for single‐bounce glossy caustics created by GGX microsurfaces. Our method is based on stochastic light culling of virtual point lights (VPLs), which is an unbiased culling method that randomly determines the range of influence of light for each VPL. While the original stochastic light culling method uses a bounding sphere defined by that light range for coarse culling (e.g., tiled culling), we have further extended the method by calculating a tighter bounding ellipsoid for glossy VPLs. Such bounding ellipsoids can be calculated analytically under the classic Phong reflection model which cannot be applied to physically plausible materials used in modern computer graphics productions. In order to use stochastic light culling for such modern materials, this paper derives a simple analytical solution to generate a tighter bounding ellipsoid for VPLs on GGX microsurfaces. This paper also presents an efficient implementation for culling bounding ellipsoids in the context of tiled culling. When stochastic light culling is combined with interleaved sampling for a scene with tens of thousands of VPLs, this tiled culling is faster than conservative rasterization‐based clustered shading which is a state‐of‐the‐art culling technique that supports bounding ellipsoids. Using these techniques, VPLs are culled efficiently for completely dynamic single‐bounce glossy caustics reflected from GGX microsurfaces.     

Bi‐Layer textures: a Model for Synthesis and Deformation of Composite Textures

We propose a bi‐layer representation for textures which is suitable for on‐the‐fly synthesis of unbounded textures from an input exemplar. The goal is to improve the variety of outputs while preserving plausible small‐scale details. The insight is that many natural textures can be decomposed into a series of fine scale Gaussian patterns which have to be faithfully reproduced, and some non‐homogeneous, larger scale structure which can be deformed to add variety. Our key contribution is a novel, bi‐layer representation for such textures. It includes a model for spatially‐varying Gaussian noise, together with a mechanism enabling synchronization with a structure layer. We propose an automatic method to instantiate our bi‐layer model from an input exemplar. At the synthesis stage, the two layers are generated independently, synchronized and added, preserving the consistency of details even when the structure layer has been deformed to increase variety. We show on a variety of complex, real textures, that our method reduces repetition artifacts while preserving a coherent appearance.     

Distortion‐Free Displacement Mapping

Displacement mapping is routinely used to add geometric details in a fast and easy‐to‐control way, both in offline rendering as well as recently in interactive applications such as games. However, it went largely unnoticed (with the exception of McGuire and Whitson [MW08]) that, when applying displacement mapping to a surface with a low‐distortion parametrization, this parametrization is distorted as the geometry was changed by the displacement mapping. Typical resulting artifacts are “rubber band”‐like distortion patterns in areas of strong displacement change where a small isotropic area in texture space is mapped to a large anisotropic area in world space. We describe a fast, fully GPU‐based two‐step procedure to resolve this problem. First, a correction deformation is computed from the displacement map. Second, two variants to apply this correction when computing displacement mapping are proposed. The first variant is backward‐compatible and can resolve the artifact in any rendering pipeline without modifying it and without requiring additional computation at render time, but only works for bijective parametrizations. The second variant works for more general parametrizations, but requires to modify the rendering code and incurs a very small computational overhead.