Abstract
We introduce Dr. Splat, a novel approach for open vocabulary 3D scene understanding leveraging 3D Gaussian Splatting.
Unlike existing language-embedded 3DGS methods, which rely on a rendering process, our method directly
associates language-aligned CLIP embeddings with 3D Gaussians for holistic 3D scene understanding.
The key of our method is a language feature registration technique where CLIP embeddings are assigned to the dominant Gaussians intersected by each pixel-ray.
Moreover, we integrate Product Quantization (PQ) trained on general large scale image data to compactly represent embeddings without per-scene optimization.
Experiments demonstrate that our approach significantly outperforms existing approaches in 3D perception benchmarks, such as open-vocabulary 3D semantic segmentation,
3D object localization, and 3D object selection tasks.
Dr. Splat
Dr. Splat directly associates CLIP embeddings with 3D Gaussians for open-vocabulary 3D scene understanding,
achieving state-of-the-art 3D perception without rendering.
3D object selection
3D object localization
3D semantic segmentation
Large scene results
BibTeX
@inproceedings{drsplat25,
title={Dr. Splat: Directly Referring 3D Gaussian Splatting via Direct Language Embedding Registration},
author={Jun-Seong, Kim and Kim GeonU and Yu-Ji, Kim and Yu-Chiang Frank Wang and Jaesung Choe and Oh, Tae-Hyun},
booktitle=CVPR,
year={2025}
}