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Published on: November 12, 2014
Hi-LSplat: Hierarchical 3D Language Gaussian Splatting
IEEE Transactions on Visualization and Computer Graphics
|March 3, 2026
Summary
Hi-LSplat introduces a novel approach for 3D open-vocabulary querying using Hierarchical Language Gaussian Splatting. This method enhances view consistency and hierarchical semantic understanding in 3D scenes.
Area of Science:
- Computer Vision
- Artificial Intelligence
- 3D Scene Understanding
Background:
- 3D Gaussian Splatting (3DGS) models for language queries face view inconsistencies due to reliance on 2D foundation models.
- Open-vocabulary challenges hinder accurate object and relational descriptions, impeding hierarchical semantic understanding in 3D scenes.
Purpose of the Study:
- To propose Hi-LSplat, a view-consistent Hierarchical Language Gaussian Splatting method for 3D open-vocabulary querying.
- To address view inconsistencies and improve hierarchical semantic understanding in 3D scenes.
Main Methods:
- Lifting 2D features to 3D by constructing a 3D hierarchical semantic tree with layered instance clustering.
- Implementing instance-wise and part-wise contrastive losses for comprehensive hierarchical semantic representation.
- Developing two novel hierarchical semantic datasets for evaluation.
Main Results:
- Hi-LSplat demonstrates superior performance in 3D open-vocabulary segmentation and localization.
- The method effectively captures complex hierarchical semantics within 3D scenes, validated by strong performance on specialized datasets.
Conclusions:
- Hi-LSplat achieves view-consistent 3D hierarchical semantics, overcoming limitations of previous 3DGS models.
- The proposed approach significantly advances 3D open-vocabulary querying and hierarchical semantic understanding.
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