361 to 370 of 395 Results
Apr 10, 2025
Dalal, Anurag, 2025, "UnrealGaussianStat: Synthetic dataset for statistical analysis on Novel View Synthesis", https://doi.org/10.18710/WSU7I6, DataverseNO, V2
The dataset comprises three dynamic scenes characterized by both simple and complex lighting conditions. The quantity of cameras ranges from 4 to 512, including 4, 6, 8, 10, 12, 14, 16, 32, 64, 128, 256, and 512. The point clouds are randomly generated. |
Apr 10, 2025 -
UnrealGaussianStat: Synthetic dataset for statistical analysis on Novel View Synthesis
Plain Text - 7.3 KB -
MD5: ae749f5cee22247c02c451b3fd89ac74
Description of the deposited data set. |
Jan 30, 2025 -
UnrealGaussianStat: Synthetic dataset for statistical analysis on Novel View Synthesis
ZIP Archive - 915.9 MB -
MD5: 391a39f3be89c4031b9ddab3ff38a6e3
Zipped files containing data for 3 unreal engine synthetic scenes. |
Apr 10, 2025
Dalal, Anurag, 2025, "ScatteringSplatting Dataset: Synthetic dataset for 3D reconstruction in scattering medium", https://doi.org/10.18710/8AS0US, DataverseNO, V1
We create a synthetic dataset using Unreal Engine 5 to evaluate 3D reconstruction under scattering media like fog and underwater conditions. It includes two scenes—an outdoor foggy environment and a realistic underwater setting—with images captured from a hemispherical camera layout. Each scene provides separate training and evaluation views, and C... |
Apr 10, 2025 -
ScatteringSplatting Dataset: Synthetic dataset for 3D reconstruction in scattering medium
Plain Text - 4.5 KB -
MD5: 9088cc5401acd3191ed2e6f059728370
Description of the deposited data set. |
Apr 10, 2025 -
ScatteringSplatting Dataset: Synthetic dataset for 3D reconstruction in scattering medium
ZIP Archive - 61.2 MB -
MD5: 6fd0f091d7a865c8c03f9b5b7eb3e81e
This is the zip file containing the images and COLMAP data for the two synthetic scenes. |
Feb 24, 2026
Mathew, Manuel Sathyajith; Kandukuri, Surya Teja; Omlin, Christian, 2026, "Python scripts for Soft Ordering 1-D CNN to Estimate the Capacity Factor of Windfarms for Identifying the Age-Related Performance Degradation.", https://doi.org/10.18710/LNXUIR, DataverseNO, V1
The supplementary materials provide the complete codebase for the Soft Ordering 1-D Convolutional Neural Network (1-D CNN) model for estimating the capacity factor of wind farms, as presented in "Soft Ordering 1-D CNN to Estimate the Capacity Factor of Windfarms for Identifying the Age-Related Performance Degradation" (PHME 2024). This research was... |
Markdown Text - 5.8 KB -
MD5: 1100ca3456432e3de7660367408bf319
Description of the dataset. |
Plain Text - 6.5 KB -
MD5: e5cab828e3e375849aec71ffd9f6c8a6
Description of the dataset. |
Python Source Code - 14.2 KB -
MD5: d69365b0d4241ba0018aae90f31927d5
Main script file |
