39-Issue 7

Permanent URI for this collection

https://diglib.eg.org/handle/10.2312/2632946

Browse

Now showing 1 - 20 of 54

Image-Driven Furniture Style for Interactive 3D Scene Modeling
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Weiss, Tomer; Yildiz, Ilkay; Agarwal, Nitin; Ataer-Cansizoglu, Esra; Choi, Jae-Woo; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
Creating realistic styled spaces is a complex task, which involves design know-how for what furniture pieces go well together. Interior style follows abstract rules involving color, geometry and other visual elements. Following such rules, users manually select similar-style items from large repositories of 3D furniture models, a process which is both laborious and time-consuming. We propose a method for fast-tracking style-similarity tasks, by learning a furniture's style-compatibility from interior scene images. Such images contain more style information than images depicting single furniture. To understand style, we train a deep learning network on a classification task. Based on image embeddings extracted from our network, we measure stylistic compatibility of furniture. We demonstrate our method with several 3D model style-compatibility results, and with an interactive system for modeling style-consistent scenes.
Generating High-quality Superpixels in Textured Images
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Zhang, Zhe; Xu, Panpan; Chang, Jian; Wang, Wencheng; Zhao, Chong; Zhang, Jian Jun; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
Superpixel segmentation is important for promoting various image processing tasks. However, existing methods still have difficulties in generating high-quality superpixels in textured images, because they cannot separate textures from structures well. Though texture filtering can be adopted for smoothing textures before superpixel segmentation, the filtering would also smooth the object boundaries, and thus weaken the quality of generated superpixels. In this paper, we propose to use the adaptive scale box smoothing instead of the texture filtering to obtain more high-quality texture and boundary information. Based on this, we design a novel distance metric to measure the distance between different pixels, which considers boundary, color and Euclidean distance simultaneously. As a result, our method can achieve high-quality superpixel segmentation in textured images without texture filtering. The experimental results demonstrate the superiority of our method over existing methods, even the learning-based methods. Benefited from using boundaries to guide superpixel segmentation, our method can also suppress noise to generate high-quality superpixels in non-textured images.
PointSkelCNN: Deep Learning-Based 3D Human Skeleton Extraction from Point Clouds
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Qin, Hongxing; Zhang, Songshan; Liu, Qihuang; Chen, Li; Chen, Baoquan; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
A 3D human skeleton plays important roles in human shape reconstruction and human animation. Remarkable advances have been achieved recently in 3D human skeleton estimation from color and depth images via a powerful deep convolutional neural network. However, applying deep learning frameworks to 3D human skeleton extraction from point clouds remains challenging because of the sparsity of point clouds and the high nonlinearity of human skeleton regression. In this study, we develop a deep learning-based approach for 3D human skeleton extraction from point clouds. We convert 3D human skeleton extraction into offset vector regression and human body segmentation via deep learning-based point cloud contraction. Furthermore, a disambiguation strategy is adopted to improve the robustness of joint points regression. Experiments on the public human pose dataset UBC3V and the human point cloud skeleton dataset 3DHumanSkeleton compiled by the authors show that the proposed approach outperforms the state-of-the-art methods.
Fast Out-of-Core Octree Generation for Massive Point Clouds
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Schütz, Markus; Ohrhallinger, Stefan; Wimmer, Michael; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
We propose an efficient out-of-core octree generation method for arbitrarily large point clouds. It utilizes a hierarchical counting sort to quickly split the point cloud into small chunks, which are then processed in parallel. Levels of detail are generated by subsampling the full data set bottom up using one of multiple exchangeable sampling strategies.We introduce a fast hierarchical approximate blue-noise strategy and compare it to a uniform random sampling strategy. The throughput, including out-of-core access to disk, generating the octree, and writing the final result to disk, is about an order of magnitude faster than the state of the art, and reaches up to around 6 million points per second for the blue-noise approach and up to around 9 million points per second for the uniform random approach on modern SSDs.
Visual Analytics in Dental Aesthetics
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Amirkhanov, Aleksandr; Bernhard, Matthias; Karimov, Alexey; Stiller, Sabine; Geier, Andreas; Gröller, Eduard; Mistelbauer, Gabriel; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
Dental healthcare increasingly employs computer-aided design software, to provide patients with high-quality dental prosthetic devices. In modern dental reconstruction, dental technicians address the unique anatomy of each patient individually, by capturing the dental impression and measuring the mandibular movements. Subsequently, dental technicians design a custom denture that fits the patient from a functional point of view. The current workflow does not include a systematic analysis of aesthetics, and dental technicians rely only on an aesthetically pleasing mock-up that they discuss with the patient, and on their experience. Therefore, the final denture aesthetics remain unknown until the dental technicians incorporate the denture into the patient. In this work, we present a solution that integrates aesthetics analysis into the functional workflow of dental technicians. Our solution uses a video recording of the patient, to preview the denture design at any stage of the denture design process. We present a teeth pose estimation technique that enables denture preview and a set of linked visualizations that support dental technicians in the aesthetic design of dentures. These visualizations assist dental technicians in choosing the most aesthetically fitting preset from a library of dentures, in identifying the suitable denture size, and in adjusting the denture position. We demonstrate the utility of our system with four use cases, explored by a dental technician. Also, we performed a quantitative evaluation for teeth pose estimation, and an informal usability evaluation, with positive outcomes concerning the integration of aesthetics analysis into the functional workflow.
Memory-Efficient Bijective Parameterizations of Very-Large-Scale Models
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Ye, Chunyang; Su, Jian-Ping; Liu, Ligang; Fu, Xiao-Ming; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
As high-precision 3D scanners become more and more widespread, it is easy to obtain very-large-scale meshes containing at least millions of vertices. However, processing these very-large-scale meshes is still a very challenging task due to memory limitations. This paper focuses on a fundamental geometric processing task, i.e., bijective parameterization construction. To this end, we present a spline-enhanced method to compute bijective and low distortion parameterizations for very-large-scale disk topology meshes. Instead of computing descent directions using the mesh vertices as variables, we estimate descent directions for each vertex by optimizing a proxy energy defined in spline spaces. Since the spline functions contain a small set of control points, it significantly decreases memory requirement. Besides, a divide-and-conquer method is proposed to obtain bijective initializations, and a submesh-based optimization strategy is developed to reduce distortion further. The capability and feasibility of our method are demonstrated over various complex models. Compared to the existing methods for bijective parameterizations of very-large-scale meshes, our method exhibits better scalability and requires much less memory.
Monocular Human Pose and Shape Reconstruction using Part Differentiable Rendering
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Wang, Min; Qiu, Feng; Liu, Wentao; Qian, Chen; Zhou, Xiaowei; Ma, Lizhuang; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
Superior human pose and shape reconstruction from monocular images depends on removing the ambiguities caused by occlusions and shape variance. Recent works succeed in regression-based methods which estimate parametric models directly through a deep neural network supervised by 3D ground truth. However, 3D ground truth is neither in abundance nor can efficiently be obtained. In this paper, we introduce body part segmentation as critical supervision. Part segmentation not only indicates the shape of each body part but helps to infer the occlusions among parts as well. To improve the reconstruction with part segmentation, we propose a part-level differentiable renderer that enables part-based models to be supervised by part segmentation in neural networks or optimization loops. We also introduce a general parametric model engaged in the rendering pipeline as an intermediate representation between skeletons and detailed shapes, which consists of primitive geometries for better interpretability. The proposed approach combines parameter regression, body model optimization, and detailed model registration altogether. Experimental results demonstrate that the proposed method achieves balanced evaluation on pose and shape, and outperforms the state-of-the-art approaches on Human3.6M, UP-3D and LSP datasets.
Diversifying Semantic Image Synthesis and Editing via Class- and Layer-wise VAEs
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Endo, Yuki; Kanamori, Yoshihiro; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
Semantic image synthesis is a process for generating photorealistic images from a single semantic mask. To enrich the diversity of multimodal image synthesis, previous methods have controlled the global appearance of an output image by learning a single latent space. However, a single latent code is often insufficient for capturing various object styles because object appearance depends on multiple factors. To handle individual factors that determine object styles, we propose a class- and layer-wise extension to the variational autoencoder (VAE) framework that allows flexible control over each object class at the local to global levels by learning multiple latent spaces. Furthermore, we demonstrate that our method generates images that are both plausible and more diverse compared to state-of-the-art methods via extensive experiments with real and synthetic datasets in three different domains. We also show that our method enables a wide range of applications in image synthesis and editing tasks.
Coarse to Fine:Weak Feature Boosting Network for Salient Object Detection
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Zhang, Chenhao; Gao, Shanshan; Pan, Xiao; Wang, Yuting; Zhou, Yuanfeng; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
Salient object detection is to identify objects or regions with maximum visual recognition in an image, which brings significant help and improvement to many computer visual processing tasks. Although lots of methods have occurred for salient object detection, the problem is still not perfectly solved especially when the background scene is complex or the salient object is small. In this paper, we propose a novel Weak Feature Boosting Network (WFBNet) for the salient object detection task. In the WFBNet, we extract the unpredictable regions (low confidence regions) of the image via a polynomial function and enhance the features of these regions through a well-designed weak feature boosting module (WFBM). Starting from a coarse saliency map, we gradually refine it according to the boosted features to obtain the final saliency map, and our network does not need any post-processing step. We conduct extensive experiments on five benchmark datasets using comprehensive evaluation metrics. The results show that our algorithm has considerable advantages over the existing state-of-the-art methods.
SRF-Net: Spatial Relationship Feature Network for Tooth Point Cloud Classification
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Ma, Qian; Wei, Guangshun; Zhou, Yuanfeng; Pan, Xiao; Xin, Shiqing; Wang, Wenping; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
3D scanned point cloud data of teeth is popular used in digital orthodontics. The classification and semantic labelling for point cloud of each tooth is a key and challenging task for planning dental treatment. Utilizing the priori ordered position information of tooth arrangement, we propose an effective network for tooth model classification in this paper. The relative position and the adjacency similarity feature vectors are calculated for tooth 3D model, and combine the geometric feature into the fully connected layers of the classification training task. For the classification of dental anomalies, we present a dental anomalies processing method to improve the classification accuracy. We also use FocalLoss as the loss function to solve the sample imbalance of wisdom teeth. The extensive evaluations, ablation studies and comparisons demonstrate that the proposed network can classify tooth models accurately and automatically and outperforms state-of-the-art point cloud classification methods.
Learning Target-Adaptive Correlation Filters for Visual Tracking
(The Eurographics Association and John Wiley & Sons Ltd., 2020) She, Ying; Yi, Yang; Gu, Jialiang; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
Correlation filters (CF) achieve excellent performance in visual tracking but suffer from undesired boundary effects. A significant amount of approaches focus on enlarging search regions to make up for this shortcoming. However, this introduces excessive background noises and misleads the filter into learning from the ambiguous information. In this paper, we propose a novel target-adaptive correlation filter (TACF) that incorporates context and spatial-temporal regularizations into the CF framework, thus learning a more robust appearance model in the case of large appearance variations. Besides, it can be effectively optimized via the alternating direction method of multipliers(ADMM), thus achieving a global optimal solution. Finally, an adaptive updating strategy is presented to discriminate the unreliable samples and alleviate the contamination of these training samples. Extensive evaluations on OTB-2013, OTB-2015, VOT-2016, VOT-2017 and TC-128 datasets demonstrate that our TACF is very promising for various challenging scenarios compared with several state-of-the-art trackers, with real-time performance of 20 frames per second(fps).
Slice and Dice: A Physicalization Workflow for Anatomical Edutainment
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Raidou, Renata Georgia; Gröller, Eduard; Wu, Hsiang-Yun; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
During the last decades, anatomy has become an interesting topic in education-even for laymen or schoolchildren. As medical imaging techniques become increasingly sophisticated, virtual anatomical education applications have emerged. Still, anatomical models are often preferred, as they facilitate 3D localization of anatomical structures. Recently, data physicalizations (i.e., physical visualizations) have proven to be effective and engaging-sometimes, even more than their virtual counterparts. So far, medical data physicalizations involve mainly 3D printing, which is still expensive and cumbersome. We investigate alternative forms of physicalizations, which use readily available technologies (home printers) and inexpensive materials (paper or semi-transparent films) to generate crafts for anatomical edutainment. To the best of our knowledge, this is the first computergenerated crafting approach within an anatomical edutainment context. Our approach follows a cost-effective, simple, and easy-to-employ workflow, resulting in assemblable data sculptures (i.e., semi-transparent sliceforms). It primarily supports volumetric data (such as CT or MRI), but mesh data can also be imported. An octree slices the imported volume and an optimization step simplifies the slice configuration, proposing the optimal order for easy assembly. A packing algorithm places the resulting slices with their labels, annotations, and assembly instructions on a paper or transparent film of user-selected size, to be printed, assembled into a sliceform, and explored. We conducted two user studies to assess our approach, demonstrating that it is an initial positive step towards the successful creation of interactive and engaging anatomical physicalizations.
InstanceFusion: Real-time Instance-level 3D Reconstruction Using a Single RGBD Camera
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Lu, Feixiang; Peng, Haotian; Wu, Hongyu; Yang, Jun; Yang, Xinhang; Cao, Ruizhi; Zhang, Liangjun; Yang, Ruigang; Zhou, Bin; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
We present InstanceFusion, a robust real-time system to detect, segment, and reconstruct instance-level 3D objects of indoor scenes with a hand-held RGBD camera. It combines the strengths of deep learning and traditional SLAM techniques to produce visually compelling 3D semantic models. The key success comes from our novel segmentation scheme and the efficient instancelevel data fusion, which are both implemented on GPU. Specifically, for each incoming RGBD frame, we take the advantages of the RGBD features, the 3D point cloud, and the reconstructed model to perform instance-level segmentation. The corresponding RGBD data along with the instance ID are then fused to the surfel-based models. In order to sufficiently store and update these data, we design and implement a new data structure using the OpenGL Shading Language. Experimental results show that our method advances the state-of-the-art (SOTA) methods in instance segmentation and data fusion by a big margin. In addition, our instance segmentation improves the precision of 3D reconstruction, especially in the loop closure. InstanceFusion system runs 20.5Hz on a consumer-level GPU, which supports a number of augmented reality (AR) applications (e.g., 3D model registration, virtual interaction, AR map) and robot applications (e.g., navigation, manipulation, grasping). To facilitate future research and reproduce our system more easily, the source code, data, and the trained model are released on Github: https://github.com/Fancomi2017/InstanceFusion.
Not All Areas Are Equal: A Novel Separation-Restoration-Fusion Network for Image Raindrop Removal
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Ren, Dongdong; Li, Jinbao; Han, Meng; Shu, Minglei; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
Detecting and removing raindrops from an image while keeping the high quality of image details has attracted tremendous studies, but remains a challenging task due to the inhomogeneity of the degraded region and the complexity of the degraded intensity. In this paper, we get rid of the dependence of deep learning on image-to-image translation and propose a separationrestoration- fusion network for raindrops removal. Our key idea is to recover regions of different damage levels individually, so that each region achieves the optimal recovery result, and finally fuse the recovered areas. In the region restoration module, to complete the restoration of a specific area, we propose a multi-scale feature fusion global information aggregation attention network to achieve global to local information aggregation. Besides, we also design an inside and outside dense connection dilated network, to ensure the fusion of the separated regions and the fine restoration of the image. The qualitatively and quantitatively evaluations are conducted to evaluate our method with the latest existing methods. The result demonstrates that our method outperforms state-of-the-art methods by a large margin on the benchmark datasets in extensive experiments.
Procedural Physically based BRDF for Real-Time Rendering of Glints
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Chermain, Xavier; Sauvage, Basile; Dischler, Jean-Michel; Dachsbacher, Carsten; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
Physically based rendering of glittering surfaces is a challenging problem in computer graphics. Several methods have proposed off-line solutions, but none is dedicated to high-performance graphics. In this work, we propose a novel physically based BRDF for real-time rendering of glints. Our model can reproduce the appearance of sparkling materials (rocks, rough plastics, glitter fabrics, etc.). Compared to the previous real-time method [ZK16], which is not physically based, our BRDF uses normalized NDFs and converges to the standard microfacet BRDF [CT82] for a large number of microfacets. Our method procedurally computes NDFs with hundreds of sharp lobes. It relies on a dictionary of 1D marginal distributions: at each location two of them are randomly picked and multiplied (to obtain a NDF), rotated (to increase the variety), and scaled (to control standard deviation/roughness). The dictionary is multiscale, does not depend on roughness, and has a low memory footprint (less than 1 MiB)
Real Time Multiscale Rendering of Dense Dynamic Stackings
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Michel, Élie; Boubekeur, Tamy; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
Dense dynamic aggregates of similar elements are frequent in natural phenomena and challenging to render under full real time constraints. The optimal representation to render them changes drastically depending on the distance at which they are observed, ranging from sets of detailed textured meshes for near views to point clouds for distant ones. Our multiscale representation use impostors to achieve the mid-range transition from mesh-based to point-based scales. To ensure a visual continuum, the impostor model should match as closely as possible the mesh on one side, and reduce to a single pixel response that equals point rendering on the other. In this paper, we propose a model based on rich spherical impostors, able to combine precomputed as well as dynamic procedural data, and offering seamless transitions from close instanced meshes to distant points. Our approach is architectured around an on-the-fly discrimination mechanism and intensively exploits the rough spherical geometry of the impostor proxy. In particular, we propose a new sampling mechanism to reconstruct novel views from the precomputed ones, together with a new conservative occlusion culling method, coupled with a two-pass rendering pipeline leveraging early-Z rejection. As a result, our system scales well and is even able to render sand, while supporting completely dynamic stackings.
A Novel Plastic Phase-Field Method for Ductile Fracture with GPU Optimization
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Zhao, Zipeng; Huang, Kemeng; Li, Chen; Wang, Changbo; Qin, Hong; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
efficiently simulate ductile fracture with GPU optimization. At the theoretical level of physically-based modeling and simulation, our PPF approach assumes the fracture sensitivity of the material increases with the plastic strain accumulation. As a result, we first develop a hardening-related fracture toughness function towards phase-field evolution. Second, we follow the associative flow rule and adopt a novel degraded von Mises yield criterion. In this way, we establish the tight coupling of the phase-field and plastic treatment, with which our PPF method can present distinct elastoplasticity, necking, and fracture characteristics during ductile fracture simulation. At the numerical level towards GPU optimization, we further devise an advanced parallel framework, which takes the full advantages of hierarchical architecture. Our strategy dramatically enhances the computational efficiency of preprocessing and phase-field evolution for our PPF with the material point method (MPM). Based on our extensive experiments on a variety of benchmarks, our novel method's performance gain can reach 1.56x speedup of the primary GPU MPM. Finally, our comprehensive simulation results have confirmed that this new PPF method can efficiently and realistically simulate complex ductile fracture phenomena in 3D interactive graphics and animation.
A Graph-based One-Shot Learning Method for Point Cloud Recognition
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Fan, Zhaoxin; Liu, Hongyan; He, Jun; Sun, Qi; Du, Xiaoyong; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
Point cloud based 3D vision tasks, such as 3D object recognition, are critical to many real world applications such as autonomous driving. Many point cloud processing models based on deep learning have been proposed by researchers recently. However, they are all large-sample dependent, which means that a large amount of manually labelled training data are needed to train the model, resulting in huge labor cost. In this paper, to tackle this problem, we propose a One-Shot learning model for Point Cloud Recognition, namely OS-PCR. Different from previous methods, our method formulates a new setting, where the model only needs to see one sample per class once for memorizing at inference time when new classes are needed to be recognized. To fulfill this task, we design three modules in the model: an Encoder Module, an Edge-conditioned Graph Convolutional Network Module, and a Query Module. To evaluate the performance of the proposed model, we build a one-shot learning benchmark dataset for 3D point cloud analysis. Then, comprehensive experiments are conducted on it to demonstrate the effectiveness of our proposed model.
Next Event Estimation++: Visibility Mapping for Efficient Light Transport Simulation
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Guo, Jerry Jinfeng; Eisemann, Martin; Eisemann, Elmar; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
Monte-Carlo rendering requires determining the visibility between scene points as the most common and compute intense operation to establish paths between camera and light source. Unfortunately, many tests reveal occlusions and the corresponding paths do not contribute to the final image. In this work, we present next event estimation++ (NEE++): a visibility mapping technique to perform visibility tests in a more informed way by caching voxel to voxel visibility probabilities. We show two scenarios: Russian roulette style rejection of visibility tests and direct importance sampling of the visibility. We show applications to next event estimation and light sampling in a uni-directional path tracer, and light-subpath sampling in Bi-Directional Path Tracing. The technique is simple to implement, easy to add to existing rendering systems, and comes at almost no cost, as the required information can be directly extracted from the rendering process itself. It discards up to 80% of visibility tests on average, while reducing variance by ~20% compared to other state-of-the-art light sampling techniques with the same number of samples. It gracefully handles complex scenes with efficiency similar to Metropolis light transport techniques but with a more uniform convergence.
Cosserat Rod with rh-Adaptive Discretization
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Wen, Jiahao; Chen, Jiong; Nobuyuki, Umetani; Bao, Hujun; Huang, Jin; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
Rod-like one-dimensional elastic objects often exhibit complex behaviors which pose great challenges to the discretization method for pursuing a faithful simulation. By only moving a small portion of material points, the Eulerian-on-Lagrangian (EoL) method already shows great adaptivity to handle sharp contact, but it is still far from enough to reproduce rich and complex geometry details arising in simulations. In this paper, we extend the discrete configuration space by unifying all Lagrangian and EoL nodes in representation for even more adaptivity with every sample being assigned with a dynamic material coordinate. However, this great extension will immediately bring in much more redundancy in the dynamic system. Therefore, we propose additional energy to control the spatial distribution of all material points, seeking to equally space them with respect to a curvature-based density field as a monitor. This flexible approach can effectively constrain the motion of material points to resolve numerical degeneracy, while simultaneously enables them to notably slide inside the parametric domain to account for the shape parameterization. Besides, to accurately respond to sharp contact, our method can also insert or remove nodes online and adjust the energy stiffness to suppress possible jittering artifacts that could be excited in a stiff system. As a result of this hybrid rh-adaption, our proposed method is capable of reproducing many realistic rod dynamics, such as excessive bending, twisting and knotting while only using a limited number of elements.

Browse

Browsing 39-Issue 7 by Issue Date

Results Per Page

Sort Options