Recent years we have witnessed rapid development in NeRF-based image
rendering due to its high quality. However, point clouds rendering is somehow
less explored. Compared to NeRF-based rendering which suffers from dense
spatial sampling, point clouds rendering is naturally less computation
intensive, which enables its deployment in mobile computing device. In this
work, we focus on boosting the image quality of point clouds rendering with a
compact model design. We first analyze the adaption of the volume rendering
formulation on point clouds. Based on the analysis, we simplify the NeRF
representation to a spatial mapping function which only requires single
evaluation per pixel. Further, motivated by ray marching, we rectify the the
noisy raw point clouds to the estimated intersection between rays and surfaces
as queried coordinates, which could avoid \textit{spatial frequency collapse}
and neighbor point disturbance. Composed of rasterization, spatial mapping and
the refinement stages, our method achieves the state-of-the-art performance on
point clouds rendering, outperforming prior works by notable margins, with a
smaller model size. We obtain a PSNR of 31.74 on NeRF-Synthetic, 25.88 on
ScanNet and 30.81 on DTU. Code and data are publicly available at
this https URL

本文旨在提升点云渲染的图像质量。我们基于容积渲染公式及空间映射的分析，简化 NeRF 表示为只需每像素一次评估的空间映射函数。同时，本文还提出了用射线行进法对嘈杂的原始点云进行矫正，以避免空间频率崩溃和邻域点干扰。我们的方法在点云渲染方面取得了最先进的性能，使用更小的模型尺寸优于之前的研究，其中包括 NeRF-Synthetic（31.74），ScanNet（25.88）和 DTU（30.81）的 PSNR。

通过辐射映射提升点云渲染效果

Boosting Point Clouds Rendering via Radiance Mapping

Neural Radiance Fields (NeRFs) have demonstrated amazing ability to
synthesize images of 3D scenes from novel views. However, they rely upon
specialized volumetric rendering algorithms based on ray marching that are
mismatched to the capabilities of widely deployed graphics hardware. This paper
introduces a new NeRF representation based on textured polygons that can
synthesize novel images efficiently with standard rendering pipelines. The NeRF
is represented as a set of polygons with textures representing binary opacities
and feature vectors. Traditional rendering of the polygons with a z-buffer
yields an image with features at every pixel, which are interpreted by a small,
view-dependent MLP running in a fragment shader to produce a final pixel color.
This approach enables NeRFs to be rendered with the traditional polygon
rasterization pipeline, which provides massive pixel-level parallelism,
achieving interactive frame rates on a wide range of compute platforms,
including mobile phones.

本文介绍了一种基于纹理多边形的新型 NeRF 表示方法，能够高效地使用标准渲染管道合成新图像，这种方法允许使用传统的多边形光栅化方法对 NeRF 进行渲染，从而在各种计算平台上实现交互式帧速率。

MobileNeRF：利用多边形光栅化管道在移动架构上高效渲染神经场

MobileNeRF: Exploiting the Polygon Rasterization Pipeline for Efficient  Neural Field Rendering on Mobile Architectures

Traditional 3D morphable face models (3DMMs) provide fine-grained control
over expression but cannot easily capture geometric and appearance details.
Neural volumetric representations approach photorealism but are hard to animate
and do not generalize well to unseen expressions. To tackle this problem, we
propose IMavatar (Implicit Morphable avatar), a novel method for learning
implicit head avatars from monocular videos. Inspired by the fine-grained
control mechanisms afforded by conventional 3DMMs, we represent the expression-
and pose- related deformations via learned blendshapes and skinning fields.
These attributes are pose-independent and can be used to morph the canonical
geometry and texture fields given novel expression and pose parameters. We
employ ray marching and iterative root-finding to locate the canonical surface
intersection for each pixel. A key contribution is our novel analytical
gradient formulation that enables end-to-end training of IMavatars from videos.
We show quantitatively and qualitatively that our method improves geometry and
covers a more complete expression space compared to state-of-the-art methods.

提出了一种名为 IMavatar 的新方法，可以从单目视频中生成隐式头像。通过学习混合形状和蒙皮场来表示表情和姿势相关的变形，使得这些属性可以用于完成新表情和姿态下的几何和纹理场的变形，并通过光线行进和迭代根查找，为每个像素定位规范表面交点，从而实现了头像的生成，并通过端到端的训练，得出了比现有技术更完整的表情空间和几何形状。