3D Gaussian Splatting has emerged as a very promising scene representation,
achieving state-of-the-art quality in novel view synthesis significantly faster
than competing alternatives. However, its use of spherical harmonics to
represent scene colors limits the expressivity of 3D Gaussians and, as a
consequence, the capability of the representation to generalize as we move away
from the training views. In this paper, we propose to encode the color
information of 3D Gaussians into per-Gaussian feature vectors, which we denote
as Feature Splatting (FeatSplat). To synthesize a novel view, Gaussians are
first "splatted" into the image plane, then the corresponding feature vectors
are alpha-blended, and finally the blended vector is decoded by a small MLP to
render the RGB pixel values. To further inform the model, we concatenate a
camera embedding to the blended feature vector, to condition the decoding also
on the viewpoint information. Our experiments show that these novel model for
encoding the radiance considerably improves novel view synthesis for low
overlap views that are distant from the training views. Finally, we also show
the capacity and convenience of our feature vector representation,
demonstrating its capability not only to generate RGB values for novel views,
but also their per-pixel semantic labels. We will release the code upon
acceptance.
Keywords: Gaussian Splatting, Novel View Synthesis, Feature Splatting

利用特征喷溅（FeatSplat）将三维高斯场的颜色信息编码到每个高斯特征向量中，进而通过小型 MLP 解码融合的特征向量以生成 RGB 像素值，且通过叠加相机嵌入来条件解码以包含视角信息。实验证明，该方法显著提高了对训练视图之外远离的低重叠视图的新视图合成，并展示了特征向量表示的能力不仅可以生成新视图的 RGB 值，还可以生成每个像素的语义标签。