3D Content
3D content generation and manipulation are active research areas aiming to create realistic and versatile three-dimensional models and scenes. Current efforts focus on improving real-time rendering, AI-assisted collaborative creation, and style transfer using techniques like Gaussian splatting and diffusion models, often incorporating 3D priors or leveraging foundation models like Segment Anything Model. These advancements are significant for various applications, including virtual and augmented reality, computer-aided design, and medical imaging, by enabling more efficient and accurate 3D content creation and analysis.
Papers
Topo-Geometrically Distinct Path Computation using Neighborhood-augmented Graph, and its Application to Path Planning for a Tethered Robot in 3D
Alp Sahin, Subhrajit Bhattacharya
Label- and slide-free tissue histology using 3D epi-mode quantitative phase imaging and virtual H&E staining
Tanishq Mathew Abraham, Paloma Casteleiro Costa, Caroline Filan, Zhe Guang, Zhaobin Zhang, Stewart Neill, Jeffrey J. Olson, Richard Levenson, Francisco E. Robles
Multiclass MRI Brain Tumor Segmentation using 3D Attention-based U-Net
Maryann M. Gitonga
When ChatGPT for Computer Vision Will Come? From 2D to 3D
Chenghao Li, Chaoning Zhang
Generative AI meets 3D: A Survey on Text-to-3D in AIGC Era
Chenghao Li, Chaoning Zhang, Joseph Cho, Atish Waghwase, Lik-Hang Lee, Francois Rameau, Yang Yang, Sung-Ho Bae, Choong Seon Hong