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RT-GS2: Real-Time Generalizable Semantic Segmentation for 3D Gaussian Representations of Radiance Fields
Our method adopts a new approach by first extracting view-independent 3D Gaussian features in a self-supervised manner, followed by a novel View-Dependent / View-Independent (VDVI) feature fusion to enhance semantic consistency over different views.
RESSCAL3D: Resolution Scalable 3D Semantic Segmentation of Point Clouds
To the best of our knowledge, the proposed method is the first to propose a resolution-scalable approach for 3D semantic segmentation of point clouds based on deep learning.
MaskLayer: Enabling scalable deep learning solutions by training embedded feature sets
To the best of our knowledge, this is the first work presenting a generic solution able to achieve quality scalable results within the deep learning framework.
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