arXiv:2312.00206v4 Announce Type: replace-cross Abstract: 3D Gaussian Splatting (3DGS) has recently enabled real-time rendering of unbounded 3D scenes for novel view synthesis. However, this technique requires dense training views to accurately reconstruct 3D geometry. A limited number of input views will significantly degrade reconstruction quality, resulting in artifacts such as "floaters" and "background collapse" at unseen viewpoints. In this work, we introduce SparseGS, an efficient training pipeline designed to address the limitations of 3DGS in scenarios with sparse training views. Spar
The rapid advancement in 3D Gaussian Splatting (3DGS) has established it as a leading technique for novel view synthesis, making improvements on its current limitations a critical next step.
SparseGS addresses a key limitation of 3DGS, enabling high-quality 3D reconstructions from fewer input views, which significantly broadens the applicability and efficiency of this technology across various sectors.
The ability to generate high-fidelity 3D scenes with fewer training images reduces data collection efforts and computational costs, making advanced 3D visual reconstruction more accessible and scalable.
- · AR/VR developers
- · 3D content creators
- · Robotics
- · AI model developers
- · Companies reliant on dense 3D scanning
- · Traditional photogrammetry services
Improved 3D reconstruction quality and efficiency from limited input data.
Accelerated development and adoption of AR/VR applications due to easier 3D asset creation.
Potential for real-time 3D reconstruction in dynamic environments with minimal sensing, advancing autonomous systems.
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Read at arXiv cs.LG