VPModel: High-Fidelity Product Simulation in a Virtual-Physical Environment

Min, Xin; Zhang, Wenqiao; Sun, Shouqian; Zhao, Nan; Tang, Siliang; Zhuang, Yueting

doi:10.1109/tvcg.2019.2932276

Cited by 16 publications

(18 citation statements)

References 36 publications

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“…Recently, a novel methodology called VPModel has also been published, which uses mixed reality (MR) through Microsoft MR HoloLens glasses, a leap motion controller and 3D printing technology to make a physical-virtual hybrid prototype capable of detecting hand gestures and recognizing actions [41]. This methodology was put into practice in the creation of a digital camera, in which the user could handle the product in a situation of use similar to the real one.…”

Section: Haptic Hybrid Prototyping (Hhp): Methods Overviewmentioning

confidence: 99%

“…This allows the user to receive the haptic and visual information he needs to perceive a situation of use similar to the real one. technology to make a physical-virtual hybrid prototype capable of detecting hand gestures and recognizing actions [41]. This methodology was put into practice in the creation of a digital camera, in which the user could handle the product in a situation of use similar to the real one.…”

Section: Haptic Hybrid Prototyping (Hhp): Methods Overviewmentioning

confidence: 99%

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Haptic Hybrid Prototyping (HHP): An AR Application for Texture Evaluation with Semantic Content in Product Design

et al. 2019

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The manufacture of prototypes is costly in economic and temporal terms and in order to carry this out it is necessary to accept certain deviations with respect to the final finishes. This article proposes haptic hybrid prototyping, a haptic-visual product prototyping method created to help product design teams evaluate and select semantic information conveyed between product and user through texturing and ribs of a product in early stages of conceptualization. For the evaluation of this tool, an experiment was realized in which the haptic experience was compared during the interaction with final products and through the HHP. As a result, it was observed that the answers of the interviewees coincided in both situations in 81% of the cases. It was concluded that the HHP enables us to know the semantic information transmitted through haptic-visual means between product and user as well as being able to quantify the clarity with which this information is transmitted. Therefore, this new tool makes it possible to reduce the manufacturing lead time of prototypes as well as the conceptualization phase of the product, providing information on the future success of the product in the market and its economic return.

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Section: Haptic Hybrid Prototyping (Hhp): Methods Overviewmentioning

confidence: 99%

Section: Haptic Hybrid Prototyping (Hhp): Methods Overviewmentioning

confidence: 99%

Haptic Hybrid Prototyping (HHP): An AR Application for Texture Evaluation with Semantic Content in Product Design

et al. 2019

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“…Novel View Synthesis for Static Scenes. Earlier approaches [15,33,4,6,9,22] tackle novel view synthesis by first building an explicit 3D reconstruction of a static scene, such as voxels and meshes, and then rendering novel views based on the reconstructed model. On the other hand, multi-plane images [49,20] represent a scene with multiple images at different depths and can reconstruct scenes with complex structures.…”

Section: Related Workmentioning

confidence: 99%

DeVRF: Fast Deformable Voxel Radiance Fields for Dynamic Scenes

Liu¹,

Cao²,

Mao³

et al. 2022

Preprint

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Modeling dynamic scenes is important for many applications such as virtual reality and telepresence. Despite achieving unprecedented fidelity for novel view synthesis in dynamic scenes, existing methods based on Neural Radiance Fields (NeRF) suffer from slow convergence (i.e., model training time measured in days). In this paper, we present DeVRF, a novel representation to accelerate learning dynamic radiance fields. The core of DeVRF is to model both the 3D canonical space and 4D deformation field of a dynamic, non-rigid scene with explicit and discrete voxelbased representations. However, it is quite challenging to train such a representation which has a large number of model parameters, often resulting in overfitting issues. To overcome this challenge, we devise a novel static → dynamic learning paradigm together with a new data capture setup that is convenient to deploy in practice. This paradigm unlocks efficient learning of deformable radiance fields via utilizing the 3D volumetric canonical space learnt from multi-view static images to ease the learning of 4D voxel deformation field with only few-view dynamic sequences. To further improve the efficiency of our DeVRF and its synthesized novel view's quality, we conduct thorough explorations and identify a set of strategies. We evaluate DeVRF on both synthetic and real-world dynamic scenes with different types of deformation. Experiments demonstrate that DeVRF achieves two orders of magnitude speedup (100× faster) with on-par high-fidelity results compared to the previous state-of-the-art approaches. The code and dataset will be released in https://github.com/showlab/DeVRF.

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“…Due to how the overlay works, virtual objects cannot completely occlude the real environment as they will always be transparent. [48], [49], [75], [76], [78] Custom User Preference 10 (11.1%) [36], [60], [67]- [69], [73], [86], [95], [97], [105] Presence Questionnaire (PQ) [57] Presence 7 [59], [60], [66], [74], [84], [ [43], [43], [56], [72], [96], [108], [113], [118], [119] Custom User Performance 7 (100%) [106], [112], [120]- [124] Simulator Sickness Questionnaire (SSQ) [125] Physiologic Responses 8 [56], [61], [66], [106], [110], [112], [126] Motion Sickness Assessment Questionnaire [127] Physiologic Responses 1 [109]…”

Section: B Immersive Technologymentioning

confidence: 99%