Teatime
*Visualization: SAM+CLIP features, reduced to 3-dimension via LangSplat autoencoder for visualization purpose, uplifted to 3D
TL;DR: Gaussian Splatting is a widely adopted approach for 3D scene representation, offering efficient, high-quality
reconstruction and rendering. A key reason for its success is the simplicity of representing scenes with sets of Gaussians,
making it interpretable and adaptable. To enhance understanding beyond visual representation, recent approaches extend Gaussian
Splatting with semantic vision-language features, enabling open-set tasks. Typically, these language features are aggregated from
multiple 2D views, however, existing methods rely on cumbersome techniques, resulting in high computational costs and longer training times.
In this work, we show that the complicated pipelines for language 3D Gaussian Splatting are simply unnecessary.
Instead, we follow a probabilistic formulation of Language Gaussian Splatting and apply Occam’s razor to the task
at hand, leading to a highly efficient weighted multi-view feature aggregation technique. Doing so offers us state-ofthe-art
results with a speed-up of two orders of magnitude without any compression, allowing for easy scene manipulation
Overview: Occam's LGS consists of two main stages: (1) Forward rendering with 3D Gaussian Splatting to obtain alpha blending weights w, projected positions xi' and pixels pi, followed by weighted aggregation of multi-view semantic features and (2) Filtering of noisy Gaussians
We show the comparison with other works.
We also show the visualization of the relevancy map for the 3D-OVS dataset.
Room Scene
Bench Scene
Lawn Scene
We select and extract an "Pocelain hand" and "Waldo" (represented by its Gaussians) from the Figurines scene of LERF. By simply copying the object's Gaussians together with their parameters and semantic features, the new object seamlessly integrates into the Teatime scene while preserving its semantic features.
@article{cheng2024occamslgssimpleapproach,
title={Occam's LGS: A Simple Approach for Language Gaussian Splatting},
author={Jiahuan Cheng and Jan-Nico Zaech and Luc Van Gool and Danda Pani Paudel},
year={2024},
eprint={2412.01807}
}