Ruofeng Tong

Collision-streams: fast GPU-based collision detection for deformable models

We present a fast GPU-based streaming algorithm to perform collision queries between deformable models. Our approach is based on hierarchical culling and reduces the computation to generating different streams. We present a novel stream registration method to compact the streams and efficiently compute the potentially colliding pairs of primitives. We also use a deferred front tracking method to lower the memory overhead. The overall algorithm has been implemented on different GPUs and we have evaluated its performance on non-rigid and deformable simulations. We highlight our speedups over prior CPU-based and GPU-based algorithms. In practice, our algorithm can perform inter-object and intra-object computations on models composed of hundreds of thousands of triangles in tens of milliseconds.

Fast continuous collision detection using deforming non-penetration filters

We present a novel culling algorithm that uses deforming non-penetration filters to improve the performance of continuous collision detection (CCD) algorithms. The underlying idea is to use a simple and effective filter that reduces both the number of false positives and the elementary tests between the primitives. This filter is derived from the coplanarity condition and can be easily combined with other methods used to accelerate CCD. We have implemented the algorithm and tested its performance on many non-rigid simulations. In practice, we can reduce the number of false positives significantly and improve the overall performance of CCD algorithms by 1.5--8.2x.

Content-aware copying and pasting in images

We present a content-aware image copy-and-paste technique which combines ideas from both matting and gradient-based methods. We modify the diffusion process used in the gradient-based approach to use the alpha matte for the cloned area as a weight function to control intensity interpolation. This ensures that the color style of the significant parts of the selected region is preserved when pasting. We use a framework based on mean-value coordinates to implement our approach, allowing us to provide a parallel implementation suitable for use on a GPU. Experimental results show the advantages of our method.

Continuous penalty forces

We present a simple algorithm to compute continuous penalty forces to determine collision response between rigid and deformable models bounded by triangle meshes. Our algorithm computes a well-behaved solution in contrast to the traditional stability and robustness problems of penalty methods, induced by force discontinuities. We trace contact features along their deforming trajectories and accumulate penalty forces along the penetration time intervals between the overlapping feature pairs. Moreover, we present a closed-form expression to compute the continuous and smooth collision response. Our method has very small additional overhead compared to previous penalty methods, and can significantly improve the stability and robustness. We highlight its benefits on several benchmarks.

VolCCD: Fast continuous collision culling between deforming volume meshes

We present a novel culling algorithm to perform fast and robust continuous collision detection between deforming volume meshes. This includes a continuous separating axis test that can conservatively check whether two volume meshes overlap during a given time interval. In addition, we present efficient methods to eliminate redundant elementary tests between the features (e.g., vertices, edges, and faces) of volume elements (e.g., tetrahedra, hexahedra, triangular prisms, etc.). Our approach is applicable to various deforming meshes, including those with changing topologies, and efficiently computes the first time of contact. We are able to perform inter-object and intra-object collision queries in models represented with tens of thousands of volume elements at interactive rates on a single CPU core. Moreover, we observe more than an order of magnitude performance improvement over prior methods.

Multi-core collision detection between deformable models

We present a new parallel algorithm for interactive and continuous collision detection between deformable models. Our algorithm performs incremental hierarchical computations between successive frames and parallelizes the computation among multiple cores on current CPUs. The main computations include front building and updating and performing the elementary tests between the triangle primitives. The overall algorithm can perform inter- and intra-object collisions at interactive rates on current commodity processors on models with many tens of thousands of triangles. In practice, the performance of our algorithm almost scales linearly with the number of cores.

A collaborative approach to assembly sequence planning

Distributed product development requires collaborative work among team members. For the sake of supporting assembly planning activities involving geographically dispersed designers, this paper presents an approach of collaborative assembly sequence planning to validate the assemblability of parts and subassemblies rapidly. In order to increase the planning efficiency and support the collaborative planning, role-based model is exploited to compress or simplify the product. In role-based model, the B-rep models are simplified according to the permissions associated with the role, so the surfaces invisible from outside of the model are removed. In collaborative planning, the planning tasks are assigned to different designers that carry out the collaborative planning, respectively. In this paper, a knowledge-based approach is proposed to the assembly sequence planning problem. This research shows that the typical or standard CSBAT (Connection Semantics Based Assembly Tree) can be applied to a given assembly problem. This paper presents the structure of the Co-ASP (Collaborative Assembly Sequence Planning System) and provides an example to illustrate the collaborative planning approach.

Fast and exact continuous collision detection with Bernstein sign classification

We present fast algorithms to perform accurate CCD queries between triangulated models. Our formulation uses properties of the Bernstein basis and Bézier curves and reduces the problem to evaluating signs of polynomials. We present a geometrically exact CCD algorithm based on the exact geometric computation paradigm to perform reliable Boolean collision queries. Our algorithm is more than an order of magnitude faster than prior exact algorithms. We evaluate its performance for cloth and FEM simulations on CPUs and GPUs, and highlight the benefits.

Environment-Sensitive cloning in images

We present an Environment-Sensitive image cloning technique which improves the previous gradient-based methods by taking into account the content of target scene. We create a reference image to represent the global feature of target image which could be further diffused into the cloned patch, and modify the diffusion process to ensure that the cloning result is seamless and natural. Specifically, we figure out an efficient solution based on Mean-Value Coordinates (MVC) to deal with the hybrid boundary, and construct a general model based on MVC to implement our image cloning, which is further applied to video cloning. Experimental results demonstrate the effectiveness of our Environment-Sensitive cloning.

Feature-preserving mesh denoising based on vertices classification

In this paper, we present an effective surface denoising method for noisy surfaces. The two key steps in this method involve feature vertex classification and an iterative, two-step denoising method depending on two feature weighting functions. The classification for feature vertices is based on volume integral invariant. With the super nature of this integral invariant, the features of vertices can be fixed with less influence of noise, and different denoising degrees can be applied to different parts of the pending surface. Compared with other methods, our approach produces better results in feature-preserving.