Dokumentdetails
ID

oai:arXiv.org:2408.04831

Thema
Computer Science - Computer Vision... Computer Science - Artificial Inte...
Autor
Du, Bi'an Meng, Lingbei Hu, Wei
Kategorie

Computer Science

Jahr

2024

Auflistungsdatum

08.01.2025

Schlüsselwörter
sparse inputs model images computer 3d
Metrisch

Zusammenfassung

Sparse-view 3D reconstruction is a major challenge in computer vision, aiming to create complete three-dimensional models from limited viewing angles.

Key obstacles include: 1) a small number of input images with inconsistent information; 2) dependence on input image quality; and 3) large model parameter sizes.

To tackle these issues, we propose a self-augmented two-stage Gaussian splatting framework enhanced with structural masks for sparse-view 3D reconstruction.

Initially, our method generates a basic 3D Gaussian representation from sparse inputs and renders multi-view images.

We then fine-tune a pre-trained 2D diffusion model to enhance these images, using them as augmented data to further optimize the 3D Gaussians.

Additionally, a structural masking strategy during training enhances the model's robustness to sparse inputs and noise.

Experiments on benchmarks like MipNeRF360, OmniObject3D, and OpenIllumination demonstrate that our approach achieves state-of-the-art performance in perceptual quality and multi-view consistency with sparse inputs.

Du, Bi'an,Meng, Lingbei,Hu, Wei, 2024, AugGS: Self-augmented Gaussians with Structural Masks for Sparse-view 3D Reconstruction

Dokumentieren

Öffnen

Teilen

Quelle

Artikel empfohlen von ES/IODE AI

Diabetes risk reduction diet and ovarian cancer risk: an Italian case-control study
ovarian cancer case-control study diabetes diet glycemic index 0 diabetes risk cancer diet ovarian