Reconstructing Detailed 3D Car Geometries from a Single 2D Image by Repurposing PIFu - Dalam bentuk buku karya ilmiah
FADRIANTO SULISTIYORAHMAN
Informasi Umum
Kode
24.04.5793
Klasifikasi
006.37 - Computer Vision
Jenis
Karya Ilmiah - Skripsi (S1) - Reference
Subjek
Image Processing - Computer Vision
Dilihat
47 kali
Informasi Lainnya
Abstraksi
<meta charset="utf-8"><span style="white-space:pre-wrap;">User generated content is an integral part of the Internet. With VR and AR technologies becoming more accessible and affordable, there will be increase in demand to create 3D content independently. Through AI, the process of creating 3D content for the average user can be made easier. In this study, we will focus on PIFu, one of the most prominent 2D to 3D reconstruction method. PIFu is and end-to-end deep learning method for 3D geometry reconstruction. It can also infer the texture of the object. PIFu originally focused on generating 3D geometries for humans wearing clothes with has intricate texture. In this study, we trained this model on car object categories to reconstruct detailed 3D car geometries. We evaluated the test results for 24 geometries, resulting in 0.046 ± 0.019 m, 0.344 ± 0.118 m, 0.677 ± 0.124 for average symmetric surface distance, hausdorff distance, and intersection over union, respectively. The geometries also have pronounced details most notably the clear separation between the wheels and the body.</span>
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Koleksi & Sirkulasi
Tersedia 1 dari total 1 Koleksi
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Pengarang
| Nama | FADRIANTO SULISTIYORAHMAN |
| Jenis | Perorangan |
| Penyunting | Bedy Purnama, Edward Ferdian |
| Penerjemah |
Penerbit
| Nama | Universitas Telkom, S1 Informatika |
| Kota | Bandung |
| Tahun | 2024 |
Sirkulasi
| Harga sewa | IDR 0,00 |
| Denda harian | IDR 0,00 |
| Jenis | Non-Sirkulasi |
