The field of 3D rendering and reconstruction is rapidly advancing, with a focus on improving the efficiency, accuracy, and realism of 3D models and scenes. Recent developments have centered around the use of Gaussian splatting, a technique that represents 3D scenes as a collection of Gaussian primitives. This approach has enabled real-time rendering and reconstruction of complex scenes, and has been applied to a variety of applications, including medical imaging, robotics, and computer vision.
Notable advancements include the development of novel architectures and algorithms for Gaussian splatting, such as Render-FM, CGS-GAN, and SplatCo, which have improved the accuracy and efficiency of 3D rendering and reconstruction. Additionally, researchers have explored the use of deep learning techniques, such as neural networks and generative models, to improve the quality and realism of 3D models and scenes.
Other significant developments include the introduction of new datasets and benchmarks, such as the LLFF and DTU benchmarks, which have enabled the evaluation and comparison of different 3D rendering and reconstruction algorithms. Furthermore, researchers have investigated the application of 3D rendering and reconstruction to various fields, including precision agriculture, sports analysis, and urban planning.
Some papers are particularly noteworthy, such as Render-FM, which proposes a novel foundation model for real-time volumetric rendering of CT scans, and CGS-GAN, which introduces a 3D consistent Gaussian splatting GAN framework for high-quality synthesis of human heads. SplatCo is also notable, as it presents a structure-view collaborative Gaussian splatting framework for high-fidelity rendering of complex outdoor environments.
