OCSplats: Observation Completeness Quantification and Label Noise Separation in 3DGS
This addresses reconstruction errors in 3DGS for real-world applications, offering a practical solution without scene-specific tuning, though it appears incremental as it builds on existing anti-noise methods.
The paper tackles the problem of label noise in 3D Gaussian Splatting (3DGS) reconstruction, such as from moving objects or shadows, by proposing OCSplats, which improves noise classification accuracy and achieves leading reconstruction performance without parameter adjustments across diverse scenarios.
3D Gaussian Splatting (3DGS) has become one of the most promising 3D reconstruction technologies. However, label noise in real-world scenarios-such as moving objects, non-Lambertian surfaces, and shadows-often leads to reconstruction errors. Existing 3DGS-Bsed anti-noise reconstruction methods either fail to separate noise effectively or require scene-specific fine-tuning of hyperparameters, making them difficult to apply in practice. This paper re-examines the problem of anti-noise reconstruction from the perspective of epistemic uncertainty, proposing a novel framework, OCSplats. By combining key technologies such as hybrid noise assessment and observation-based cognitive correction, the accuracy of noise classification in areas with cognitive differences has been significantly improved. Moreover, to address the issue of varying noise proportions in different scenarios, we have designed a label noise classification pipeline based on dynamic anchor points. This pipeline enables OCSplats to be applied simultaneously to scenarios with vastly different noise proportions without adjusting parameters. Extensive experiments demonstrate that OCSplats always achieve leading reconstruction performance and precise label noise classification in scenes of different complexity levels.