  We develop an effective point cloud rendering pipeline for novel view
synthesis, which enables high fidelity local detail reconstruction, real-time
rendering and user-friendly editing. In the heart of our pipeline is an
adaptive frequency modulation module called Adaptive Frequency Net (AFNet),
which utilizes a hypernetwork to learn the local texture frequency encoding
that is consecutively injected into adaptive frequency activation layers to
modulate the implicit radiance signal. This mechanism improves the frequency
expressive ability of the network with richer frequency basis support, only at
a small computational budget. To further boost performance, a preprocessing
module is also proposed for point cloud geometry optimization via point opacity
estimation. In contrast to implicit rendering, our pipeline supports
high-fidelity interactive editing based on point cloud manipulation. Extensive
experimental results on NeRF-Synthetic, ScanNet, DTU and Tanks and Temples
datasets demonstrate the superior performances achieved by our method in terms
of PSNR, SSIM and LPIPS, in comparison to the state-of-the-art.
