Real-Time Omnidirectional Roaming in Large Scale Indoor Scenes

Neural radiance field (NeRF) has recently achieved impressive results in novel view synthesis. However, previous works on NeRF mainly focus on object-centric scenarios. They would suffer observable performance degradation in outward-facing and large-scale scenes due to limiting positional encoding capacity. To narrow the gap, we explore radiance fields in a geometry-aware fashion. We estimate explicit geometry from the omnidirectional neural radiance field that was learned from multiple 360° images. Relying on the recovered geometry, we use an adaptive divide-and-conquer strategy to slim and fine-tune the radiance fields and further improve render speed and quality. Quantitative and qualitative comparisons among baselines illustrated our predominant performance in large-scale indoor scenes and our system supports real-time VR roaming.