Enhancing 3D Gaussian splatting for low-quality images: semantically guided training and unsupervised quality assessment

This paper proposes CLIPGaussian, an improved training and rendering method for 3D Gaussian Splatting tailored to address the degradation of reconstruction quality under low-quality image conditions. By integrating a multi-view semantic evaluation module based on CLIP and a 3D spatial consistency loss, CLIPGaussian enhances the model’s sensitivity to object-level semantics, enabling better reconstruction even with limited or blurred training images. Furthermore, we introduce an unsupervised evaluation method leveraging semantic features and explicit 3D Gaussian properties to assess reconstruction quality, providing practical guidance in scenarios where ground truth is unavailable. Experiments on the Mip-NeRF360 dataset demonstrate CLIPGaussian’s superiority, particularly in low-quality image scenarios, validating both the training and evaluation approaches. This work paves the way for more robust and reliable 3D scene reconstruction from imperfect data. Our source code and pre-trained models are available at https://github.com/ZhChessOvO/CLIPGaussian .