T-3DGS: Removing Transient Objects for 3D Scene Reconstruction (2412.00155v2)

Abstract: Transient objects in video sequences can significantly degrade the quality of 3D scene reconstructions. To address this challenge, we propose T-3DGS, a novel framework that robustly filters out transient distractors during 3D reconstruction using Gaussian Splatting. Our framework consists of two steps. First, we employ an unsupervised classification network that distinguishes transient objects from static scene elements by leveraging their distinct training dynamics within the reconstruction process. Second, we refine these initial detections by integrating an off-the-shelf segmentation method with a bidirectional tracking module, which together enhance boundary accuracy and temporal coherence. Evaluations on both sparsely and densely captured video datasets demonstrate that T-3DGS significantly outperforms state-of-the-art approaches, enabling high-fidelity 3D reconstructions in challenging, real-world scenarios.

• The paper presents an unsupervised two-step approach that detects and removes transient objects for improved 3D scene reconstructions.
• It employs segmentation and mask propagation using the Segment Anything Model to accurately isolate static scene elements from video sequences.
• Evaluation on complex datasets shows a mean PSNR of 28.03 and SSIM of 0.96, demonstrating significant improvements over previous methods.

T-3DGS: Removing Transient Objects for 3D Scene Reconstruction

The paper on "T-3DGS: Removing Transient Objects for 3D Scene Reconstruction" presents a sophisticated approach to enhancing the precision and quality of 3D scene reconstruction by effectively handling transient objects. Traditional methods, such as NeRF and 3D Gaussian Splatting (3DGS), often struggle with dynamic scenes, leading to blurred reconstructions due to transient objects. This work introduces a novel framework that integrates unsupervised learning to differentiate static components from transient distractors, thereby resolving a critical challenge in producing high-fidelity reconstructions from real-world video data.

Methodology Overview

The central innovation in T-3DGS involves a two-step methodology: an unsupervised transient detection strategy followed by a segregated mask refinement and propagation process. The first stage utilizes a classification network, trained unsupervised, to detect transient objects based on unique training characteristics within the 3D Gaussian Splatting reconstruction context. This detection is complemented by integrating a segmentation approach, enhancing boundary precision and temporal object tracking through video frames.

The second phase significantly improves upon prior methodologies, particularly through a mask propagation technique that leverages the Segment Anything Model (SAM) to yield more consistent and accurate masks. This refinement facilitates the reliable tracking of semi-transient objects, which intermittently change states between dynamic and static within captured sequences. The robustness of this two-step method is demonstrated across complex datasets, offering substantial improvements over existing methods.

Results and Implications

The paper provides quantitative results showcasing the method's superiority. In particular, on the challenging dataset T-3DGS, which incorporates various dynamic distractions, the proposed method records a mean PSNR of 28.03 and an SSIM of 0.96, outperforming state-of-the-art alternatives by notable margins. Such metrics underscore the effectiveness of the T-3DGS approach in preserving scene integrity while accurately removing transient artifacts, thus paving the way for its application in environments with dense transient activity.

The implications of this work are extensive. Practically, the ability to reliably reconstruct static parts of scenes in the presence of transient objects opens avenues in augmented reality, virtual tourism, and autonomous navigation. Theoretically, the introduction of robust semantic feature-based unsupervised training and the refinement of transient masks elevate the understanding of temporal dynamics within scene reconstruction frameworks.

Future Perspectives

Considering the challenges in transient and semi-transient mixed environments, T-3DGS establishes a solid foundation for future exploration. Potential developments could focus on enhancing feature extraction granularity, addressing small object inconsistencies due to patch-based DINOv2 features, and refining the temporal mask propagation to better handle fluctuating object sizes due to perspective shifts. These enhancements could further substantiate the method's applicability in broader real-world scenarios, driving innovation in dynamic scene analysis.

Overall, T-3DGS stands as a significant contribution to 3D scene reconstruction, addressing crucial limitations in transient object handling with a robust, technically sound approach. This advancement significantly enhances the practicality and accuracy of deploying 3D reconstruction models in uncontrolled, real-world settings.