Differentiable Volume Rendering

Differentiable volume rendering is a technique using neural networks to represent 3D scenes and synthesize novel views, enabling efficient and accurate rendering by differentiating through the rendering process. Current research focuses on improving rendering speed and quality through novel primitive representations (e.g., Gaussian ellipsoids, microflakes), developing more robust and generalizable models for various tasks (e.g., surface reconstruction, 3D object manipulation), and incorporating additional cues like shadows to enhance scene understanding. This approach has significant implications for various fields, including computer graphics, robotics, and medical imaging, by facilitating high-fidelity 3D scene reconstruction and manipulation from limited data.