An optimization-first analysis of why VGGT's simpler architecture produces more geometrically consistent 3D reconstructions from few images than its higher-capacity successors — examining training dynamics, basin formation, gradient alignment, and the cost of abandoning a shared backbone.
A comprehensive feasibility study on using WiFi Channel State Information for dense 3D geometry reconstruction via neural implicit representations — covering the RF perception lineage, neural rendering, non-visual NeRF analogues, dataset gaps, proposed architecture, and a research roadmap.
This is a summary from my understanding of reinforcement learning, based on the book Reinforcement Learning: An Introduction by Sutton and Barto, and supplemented with the YouTube series.