Point-based Acoustic Scattering for Interactive Sound Propagation via Surface Encoding
Authors: Hsien-Yu Meng, Zhenyu Tang, Dinesh Manocha
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We have analyzed the accuracy and perform validation on thousands of unseen 3D objects and highlight the benefits over other point-based geometric deep learning methods. ... We have evaluated the performance on thousands of objects that are very different from the training database (with varying convexity, genus, shape, size, orientation) and observe high accuracy. We also perform an ablation study to highlight the benefits of our approach. |
| Researcher Affiliation | Academia | University of Maryland, College Park {mengxy19,zhy,dmanocha}@umd.edu |
| Pseudocode | No | No explicit pseudocode or algorithm blocks were found. |
| Open Source Code | No | No concrete access to source code (specific repository link or explicit code release statement) for the methodology described in this paper was found. |
| Open Datasets | Yes | We sample 100,000 3D objects from the ABC Dataset [Koch et al., 2019]. |
| Dataset Splits | Yes | We split our dataset into training, validation and test set following a 8:1:1 ratio. |
| Hardware Specification | Yes | The additional runtime overhead of estimating the scattering field from neural networks is less than 1ms per object on a NVIDIA Ge Force RTX 2080 Ti GPU." and "Our neural network is trained on an NVIDIA Ge Force RTX 2080 Ti GPU |
| Software Dependencies | No | The paper mentions 'Fast BEM Acoustics' and 'pyshtools' (with a URL), but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | The Adam optimizer is used and it decays exponentially with decay rate and decay step equal to 0.9 and 10 #training examples, respectively. |