Efficient 3D scene representation has become a key challenge with the rise of 3D Gaussian Splatting (3DGS), particularly when incorporating semantic information into the scene representation. Existing 3DGS-based methods embed both color and high-dimensional semantic features into a single field, leading to significant storage and computational overhead. To mitigate this, we propose Decoupled Feature 3D Gaussian Splatting (DF-3DGS), a novel method that decouples the color and semantic fields, thereby reducing the number of 3D Gaussians required for semantic representation. We then introduce a hierarchical compression strategy that first employs our novel quantization approach with dynamic codebook evolution to reduce data size, followed by a scene-specific autoencoder for further compression of the semantic feature dimensions. This multi-stage approach results in a compact representation that enhances both storage efficiency and reconstruction speed. Experimental results demonstrate that DF-3DGS outperforms previous 3DGS-based methods, achieving faster training and rendering times while requiring less storage, without sacrificing performance—in fact, it improves performance in the novel view semantic segmentation task. Specifically, DF-3DGS achieves remarkable improvements over Feature 3DGS, reducing training time by 10× and storage by 20×, while improving the mIoU of novel view semantic segmentation by 4%. Code is available at https://github.com/dai647/DF_3DGS.

In this paper, we present the One-shot In-context Part Segmentation (OIParts) framework, designed to tackle the challenges of part segmentation by leveraging visual foundation models (VFMs). Existing training-based one-shot part segmentation methods that utilize VFMs encounter difficulties when faced with scenarios where the one-shot image and test image exhibit significant variance in appearance and perspective, or when the object in the test image is partially visible. We argue that training on the one-shot example often leads to overfitting, thereby compromising the model's generalization capability. Our framework offers a novel approach to part segmentation that is training-free, flexible, and data-efficient, requiring only a single in-context example for precise segmentation with superior generalization ability. By thoroughly exploring the complementary strengths of VFMs, specifically DINOv2 and Stable Diffusion, we introduce an adaptive channel selection approach by minimizing the intra-class distance for better exploiting these two features, thereby enhancing the discriminatory power of the extracted features for the fine-grained parts. We have achieved remarkable segmentation performance across diverse object categories. The OIParts framework not only eliminates the need for extensive labeled data but also demonstrates superior generalization ability. Through comprehensive experimentation on three benchmark datasets, we have demonstrated the superiority of our proposed method over existing part segmentation approaches in one-shot settings.