- The paper introduces a robust loss function and clustering techniques to effectively exclude transient distractors from 3D reconstruction.
- It employs both spatial and spatio-temporal clustering with semantic features and scheduled sampling to enhance performance in casual capture scenarios.
- Empirical results on RobustNeRF and NeRF on-the-go benchmarks show significant improvements in PSNR, SSIM, and LPIPS compared to baseline methods.
SpotlessSplats: Ignoring Distractors in 3D Gaussian Splatting
The paper "SpotlessSplats: Ignoring Distractors in 3D Gaussian Splatting" addresses a significant challenge in the domain of 3D reconstruction using 3D Gaussian Splatting (3DGS): the adverse impact of transient distractors such as moving objects and lighting inconsistencies on reconstruction quality. The authors propose a novel method, SpotlessSplats (SLS), which leverages pre-trained semantic features and robust optimization techniques to mitigate these issues and achieve state-of-the-art performance in casual capture scenarios.
3DGS has garnered attention for its efficient training and rendering speeds, making it suitable for real-time applications. However, its reliance on highly controlled environments where inter-view consistency is maintained poses a limitation for real-world applications. SpotlessSplats overcomes this by integrating robust optimization techniques with pre-computed semantic features from text-to-image models.
Methodology
SpotlessSplats introduces a robust loss function that excludes transient distractors during training. The core idea revolves around two alternative approaches to detect and mask out transient effects:
- Spatial Clustering:
- This method involves over-segmenting input images into clusters using agglomerative clustering on pre-computed feature maps derived from a text-to-image model, such as Stable Diffusion.
- The clustering aims to maintain the semantic structure while delineating regions impacted by transient occluders.
- The predicted clusters are then used to compute a robust inlier/outlier mask that drives the 3DGS training.
- Spatio-temporal Clustering:
- In this approach, the authors train a Multi-Layer Perceptron (MLP) to predict pixel-wise inlier probabilities based on the semantic features.
- This MLP is trained concurrently with the 3DGS model using a self-supervised loss derived from image residuals.
- The training is structured as an alternating optimization, where the MLP and 3DGS model parameters are updated iteratively.
Key adaptations are implemented to make these approaches effective within the 3DGS framework:
- Scheduled Sampling: Gradually applying the robust masks to avoid early commitment to inaccurate outliers.
- Utilization-based Pruning: A method to reduce the number of Gaussians in the representation by tracking gradient utilization, which replaces the traditional opacity reset.
- Appearance Modeling: Adaptation of latent appearance embeddings to account for photometric inconsistencies across captures, ensuring the robust optimization does not overfit to transient-induced errors.
Empirical Results
The authors rigorously evaluate SpotlessSplats on several benchmarks, including the RobustNeRF and NeRF on-the-go datasets, which contain various levels of transient occlusions. SpotlessSplats consistently outperforms both baseline 3DGS and existing robust NeRF methods, demonstrating superior reconstruction quality.
- On the RobustNeRF dataset, SpotlessSplats achieves substantial improvements in PSNR, SSIM, and LPIPS metrics, closely approaching the "clean" models trained on curated, distractor-free data.
- In the NeRF on-the-go dataset, SpotlessSplats shows robust performance across different levels of transient occlusion, significantly improving over vanilla 3DGS and competing robust NeRF methods.
Implications and Future Work
The proposed SpotlessSplats framework highlights several important implications for practical and theoretical developments in AI:
- Practical Implications: The robust handling of transient distractors makes 3DGS more viable for real-world applications, particularly in scenarios where controlled environments are infeasible. The reduction in computational requirements via utilization-based pruning further enhances its applicability.
- Theoretical Implications: The integration of pre-trained semantic features from text-to-image models for robust optimization introduces a new dimension to 3D reconstruction techniques. The effectiveness of semantic-driven clustering in improving reconstruction quality underscores the potential of such models in enhancing robustness against real-world inconsistencies.
Future research directions may include further refinement of semantic feature extraction to handle more complex and varied transient distractors, as well as exploring more sophisticated pruning strategies to balance computational efficiency with reconstruction fidelity. The potential integration with other emerging AI techniques might also be considered to further enhance the robustness and efficiency of 3DGS in diverse and dynamic environments.
Overall, SpotlessSplats represents a significant advancement in 3D reconstruction methodologies, providing a robust and efficient framework capable of handling the inherent challenges of casual captures in less controlled settings.