Gaussian Splatting with NeRF-based Color and Opacity
Dawid Malarz, Weronika Smolak, Jacek Tabor, S{\l}awomir Tadeja,, Przemys{\l}aw Spurek

TL;DR
This paper introduces a hybrid 3D rendering model combining Gaussian Splatting and NeRF techniques, enabling efficient, high-quality rendering with improved shadow, reflection, and transparency effects without complex textures.
Contribution
The paper proposes Viewing Direction Gaussian Splatting (VDGS), a novel hybrid model that integrates Gaussian Splatting with NeRF-based color and opacity encoding for enhanced 3D rendering.
Findings
Faster training and inference compared to traditional NeRFs
Improved rendering of shadows, reflections, and transparency
Reduces the number of Gaussian components needed for conditioning
Abstract
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…
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Taxonomy
TopicsImage Processing Techniques and Applications · Industrial Vision Systems and Defect Detection
MethodsSparse Evolutionary Training
