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
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
2D and 3D CNN-Based Fusion Approach for COVID-19 Severity Prediction from 3D CT-Scans
Fares Bougourzi, Fadi Dornaika, Amir Nakib, Cosimo Distante, Abdelmalik Taleb-Ahmed
Quality evaluation of point clouds: a novel no-reference approach using transformer-based architecture
Marouane Tliba, Aladine Chetouani, Giuseppe Valenzise, Frederic Dufaux