GPU based real-time simulation of massive falling leaves

Li, Chengyang; Qian, Jing-Ye; Tong, Ruofeng; Chang, Jian; Zhang, Jianjun

doi:10.1007/s41095-015-0025-1

Cited by 4 publications

(5 citation statements)

References 16 publications

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“…The leaves falling results of (Quigley, 2017) In addition, the trajectories of leaves fluttering defined in our method are basic processes, but the mixture model of them can adequately express the complex process of leaves falling in natural scene. The leaves falling trajectories of others methods, such as in (Li, 2015b), can be split into our three basic trajectories. As shown is Figure 11, Li et al (Li, 2015b) divided leaf falling motions into 6 classes, including steady descent, periodic tumbling, transitional chaotic, periodic fluttering, transitional helical, and periodic spiral.…”

Section: The Analysis Of Experimental Resultsmentioning

confidence: 99%

“…The leaves falling trajectories of others methods, such as in (Li, 2015b), can be split into our three basic trajectories. As shown is Figure 11, Li et al (Li, 2015b) divided leaf falling motions into 6 classes, including steady descent, periodic tumbling, transitional chaotic, periodic fluttering, transitional helical, and periodic spiral. While these motions are all composed of rotation, roll and screw roll, as shown in Figure 4.…”

Section: The Analysis Of Experimental Resultsmentioning

confidence: 99%

“…Alternatively, there has a data-driven approach as in (Martin, 2015) or even randomly sample prescribed spline curves as in (Li, 2015b).…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

A Modeling Method of Fluttering Leaves Based on Point Cloud

Tang

Wang

Zhao

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Leaves falling gently or fluttering are common phenomenon in nature scenes. The authenticity of leaves falling plays an important part in the dynamic modeling of natural scenes. The leaves falling model has a widely applications in the field of animation and virtual reality. We propose a novel modeling method of fluttering leaves based on point cloud in this paper. According to the shape, the weight of leaves and the wind speed, three basic trajectories of leaves falling are defined, which are the rotation falling, the roll falling and the screw roll falling. At the same time, a parallel algorithm based on OpenMP is implemented to satisfy the needs of real-time in practical applications. Experimental results demonstrate that the proposed method is amenable to the incorporation of a variety of desirable effects.

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Section: The Analysis Of Experimental Resultsmentioning

confidence: 99%

Section: The Analysis Of Experimental Resultsmentioning

confidence: 99%

See 1 more Smart Citation

A Modeling Method of Fluttering Leaves Based on Point Cloud

Tang

Wang

Zhao

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…In addition to overcoming the limitations, we feel that it is possible to further improve the performance by exploiting the memory hierarchy of GPUs. The sparse matrix assembly and linear system solving algorithms could also be useful for FEM and other simulations [ACF11, WBS*13, LQT*15]. It would be useful to combine our approach with adaptive meshes [NSO12] and/or data‐driven methods [FYK10, dASTH10, KGBS11, ZBO13, KKN*13] to further improve the performance and realism.…”

Section: Discussionmentioning

confidence: 99%

CAMA: Contact‐Aware Matrix Assembly with Unified Collision Handling for GPU‐based Cloth Simulation

Tang

Wang

et al. 2016

Computer Graphics Forum

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Figure 1: Benchmark Andy: Our GPU-based approach can simulate the clothes dressed on a Kung-Fu boy. The meshes of three cloth pieces are represented by 127K triangles. Our simulator performs all of the computations, including implicit time integration and collision handling, in 2.42s per frame (on average) on an NVIDIA Telsa K40c GPU. Our new parallel algorithms for sparse matrix assembly and collision handling result in significant speedups over prior methods. AbstractWe present a novel GPU-based approach to robustly and efficiently simulate high-resolution and complexly layered cloth.The key component of our formulation is a parallelized matrix assembly algorithm that can quickly build a large and sparse matrix in a compressed format and accurately solve linear systems on GPUs. We also present a fast and integrated solution for parallel collision handling, including collision detection and response computations, which utilizes spatio-temporal coherence. We combine these algorithms as part of a new cloth simulation pipeline that incorporates contact forces into implicit time integration for collision avoidance. The entire pipeline is implemented on GPUs, and we evaluate its performance on complex benchmarks consisting of 100 − 300K triangles. In practice, our system takes a few seconds to simulate one frame of a complex cloth scene, which represents significant speedups over prior CPU and GPU-based cloth simulation systems. † {tang_m, tangle,

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“…The motion of the descending foliage demonstrates intriguing oscillations, rolling, and spiral motions, alongside unpredictable transitions between these phases, encompassing random occurrences, sudden changes in velocity, and similar phenomena [32], [33]. Numerous computational methods exist for replicating the state of falling leaves, with a significant portion involving the calculation of forces and moments exerted on the leaves [34], [35], [36]. In this regard, the leaves are regarded as rigid entities with a negligible thickness assumed to be zero, which is solely characterized by their length and mass.…”

Section: A Inspirationmentioning

confidence: 99%

Leaf in Wind Optimization: A New Metaheuristic Algorithm for Solving Optimization Problems

Fang,

Cao

2024

IEEE Access

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This study introduces a novel metaheuristic algorithm, Leaf in Wind Optimization, inspired by the natural phenomenon of falling leaves in the wind. The proposed method simulates the motion response of leaves in varying intensities of wind by establishing models for light wind-driven blades and strong wind-driven blades. The algorithm incorporates motion modes of linear translation and spiral rotation induced by wind, offering a hybrid search framework suitable for both strategies. This approach enables enhanced exploration and exploitation of the search space. The algorithm's performance was evaluated using three challenging benchmark test sets, CEC 2017, CEC 2019 and CEC 2022, as well as an engineering practical problem. Its effectiveness was assessed through comparison with 10 random optimization algorithms, namely: Tree Seed Algorithm, Multi-Verse Optimizer, Salp Swarm Algorithm, Artificial Ecosystem-based Optimization, Hunger Games Search, Fox Optimizer, Spider Wasp Optimizer, AOBLMOA, Enhanced Snake Optimizer, and IbI Logic Algorithm. In the comprehensive testing conducted, the proposed algorithm consistently outperformed other optimizers in approximately 82% of comparisons. Through examination of convergence curves and statistical data, it is evident that Leaf in Wind Optimization demonstrates superior potential compared to the alternative optimizers under consideration.

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GPU based real-time simulation of massive falling leaves

Cited by 4 publications

References 16 publications

A Modeling Method of Fluttering Leaves Based on Point Cloud

A Modeling Method of Fluttering Leaves Based on Point Cloud

CAMA: Contact‐Aware Matrix Assembly with Unified Collision Handling for GPU‐based Cloth Simulation

Leaf in Wind Optimization: A New Metaheuristic Algorithm for Solving Optimization Problems

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