  Neural Radiance Fields (NeRFs) have demonstrated the remarkable potential of
neural networks to capture the intricacies of 3D objects. By encoding the shape
and color information within neural network weights, NeRFs excel at producing
strikingly sharp novel views of 3D objects. Recently, numerous generalizations
of NeRFs utilizing generative models have emerged, expanding its versatility.
In contrast, Gaussian Splatting (GS) offers a similar render quality with
faster training and inference as it does not need neural networks to work. It
encodes information about the 3D objects in the set of Gaussian distributions
that can be rendered in 3D similarly to classical meshes. Unfortunately, GS are
difficult to condition since they usually require circa hundred thousand
Gaussian components. To mitigate the caveats of both models, we propose a
hybrid model Viewing Direction Gaussian Splatting (VDGS) that uses GS
representation of the 3D object's shape and NeRF-based encoding of color and
opacity. Our model uses Gaussian distributions with trainable positions (i.e.
means of Gaussian), shape (i.e. covariance of Gaussian), color and opacity, and
a neural network that takes Gaussian parameters and viewing direction to
produce changes in the said color and opacity. As a result, our model better
describes shadows, light reflections, and the transparency of 3D objects
without adding additional texture and light components.
