Recently neural rendering has attracted great attention and demonstrated impressive rendering quality. Such learned view synthesis methods exploit neural networks to implicitly represent the structure and appearance of captured objects. The neural radiance field, pioneered by NeRF [Mildenhall et al. 2020] is currently the most promising path. Instead of pursuing precise geometry reconstruction, NeRF utilizes volume rendering techniques and multi-layer perceptrons to regress density and view-dependent color per ray. By densely sampling along each ray, it can generate photorealistic results even when encountered with light reflection, transparent objects, and thin structures. To reduce the long training and inference time, the following NeRF-based methods Liu et al. 2020;Pumarola et al. 2021;] have made great efforts and modifications to increase rendering quality and speed. However, most of them focus on object-centric or small-scale scenes where placements of cameras are constrained during the
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