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
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
TextMesh: Generation of Realistic 3D Meshes From Text Prompts
Christina Tsalicoglou, Fabian Manhardt, Alessio Tonioni, Michael Niemeyer, Federico Tombari
Segment Anything in 3D with Radiance Fields
Jiazhong Cen, Jiemin Fang, Zanwei Zhou, Chen Yang, Lingxi Xie, Xiaopeng Zhang, Wei Shen, Qi Tian
Evolving Three Dimension (3D) Abstract Art: Fitting Concepts by Language
Yingtao Tian
Monocular 3D Object Detection with Bounding Box Denoising in 3D by Perceiver
Xianpeng Liu, Ce Zheng, Kelvin Cheng, Nan Xue, Guo-Jun Qi, Tianfu Wu
CG-3DSRGAN: A classification guided 3D generative adversarial network for image quality recovery from low-dose PET images
Yuxin Xue, Yige Peng, Lei Bi, Dagan Feng, Jinman Kim