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

Vadim Pryadilshchikov1, Alexander Markin1, Artem Komarichev1, Ruslan Rakhimov2, Peter Wonka3, Evgeny Burnaev1,4,
1Skoltech, Russia, 2T-Tech, Russia 3KAUST, Saudi Arabia 4AIRI, Russia
Paper arXiv Code Data

Abstract

We propose a novel framework to remove transient objects from input videos for 3D scene 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.

overview

On-the-go dataset Visual Comparisons

Ours
SpotLessSplats
Ours
SpotLessSplats
Ours
SpotLessSplats
Ours
WildGaussians

Robust Mask Propagation

Visual Comparisons of TMR

Without TMR
With TMR
Without TMR
With TMR

BibTeX


        @misc{pryadilshchikov2024t3dgsremovingtransientobjects,
              title={T-3DGS: Removing Transient Objects for 3D Scene Reconstruction}, 
              author={Vadim Pryadilshchikov and Alexander Markin and Artem Komarichev and Ruslan Rakhimov and Peter Wonka and Evgeny Burnaev},
              year={2024},
              eprint={2412.00155},
              archivePrefix={arXiv},
              primaryClass={cs.CV},
              url={https://arxiv.org/abs/2412.00155}, 
        }