Neural Radiance Fields for View Synthesis

Explore a comprehensive talk on Neural Radiance Fields (NeRFs) for view synthesis, presented by Matthew Tancik from UC Berkeley at the Tübingen seminar series of the Autonomous Vision Group. Delve into state-of-the-art techniques for synthesizing novel views of complex scenes using sparse input views and optimizing continuous volumetric scene functions. Learn about the combination of coordinate-based neural representations with classic volumetric rendering methods, and discover the importance of mapping input coordinates to higher-dimensional spaces using Fourier features. Gain insights into Neural Tangent Kernel analysis and its equivalence to transforming networks into stationary kernels with tunable bandwidth. Examine the problem of novel view interpolation, GB-alpha volume rendering, neural networks as continuous shape representations, and the optimization process using gradient descent on rendering loss. Investigate the concept of Fourier Features and their impact on network learning, including input mapping, kernel regression approximation, and the effects of kernel spectrum on convergence and generalization. Explore practical applications in 2D images, 3D shapes, and indirect supervision tasks.