CCL-LGS: Contrastive Codebook Learning for 3D Language Gaussian Splatting

TL;DR

CCL-LGS introduces a view-consistent semantic supervision framework for 3D Gaussian splatting, leveraging multi-view semantic cues and contrastive learning to improve 3D semantic understanding and rendering quality.

Contribution

It proposes a novel contrastive codebook learning approach that enforces semantic consistency across views, addressing limitations of previous 2D prior-based methods.

Findings

Outperforms previous state-of-the-art methods in 3D semantic tasks.

Effectively resolves semantic conflicts across views.

Enhances the quality of 3D Gaussian semantic fields.

Abstract

Recent advances in 3D reconstruction techniques and vision-language models have fueled significant progress in 3D semantic understanding, a capability critical to robotics, autonomous driving, and virtual/augmented reality. However, methods that rely on 2D priors are prone to a critical challenge: cross-view semantic inconsistencies induced by occlusion, image blur, and view-dependent variations. These inconsistencies, when propagated via projection supervision, deteriorate the quality of 3D Gaussian semantic fields and introduce artifacts in the rendered outputs. To mitigate this limitation, we propose CCL-LGS, a novel framework that enforces view-consistent semantic supervision by integrating multi-view semantic cues. Specifically, our approach first employs a zero-shot tracker to align a set of SAM-generated 2D masks and reliably identify their corresponding categories. Next, we utilize CLIP to extract robust semantic encodings across views. Finally, our Contrastive Codebook Learning (CCL) module distills discriminative semantic features by enforcing intra-class compactness and inter-class distinctiveness. In contrast to previous methods that directly apply CLIP to imperfect masks, our framework explicitly resolves semantic conflicts while preserving category discriminability. Extensive experiments demonstrate that CCL-LGS outperforms previous state-of-the-art methods. Our project page is available at https://epsilontl.github.io/CCL-LGS/.