TL;DR
Crowdsourced Radiance Fields (CSRF), a framework designed to harness CSVVs, substantially expands the potential data source for 3D environment reconstruction using street view videos, while the CS3D100 benchmark establishes a robust foundation for future improvements in this direction.
VDGS introduces visibility-driven statistics for scene anchors to quantify supervision strength and is leveraged for scene partitioning and for gradient compensation in under-optimized regions, thereby promoting balanced optimization across different regions.
This work proposes GSPotential, a framework that quantifies view-space supervision imbalance using a Camera Potential Field, and uses the potential field to guide reconstruction from two complementary aspects.
Novel view synthesis from sparse inputs remains challenging for 3D Gaussian Splatting (3DGS) due to ambiguous geometry, cross-view inconsistency, and missing details in under-constrained regions, resulting in degraded reconstruction and unstable rendering. To tackle these issues, we propose D$^{3}$GS, a Depth-DINO-Diff...
While radiance field representations have achieved remarkable success in photo-realistic novel view synthesis with densely captured images, their performance sharply degrades under sparse input conditions, resulting in floater artifacts and missing regions caused by its inherent shape-radiance ambiguities and lack of i...
A semantic-guided 3D Gaussian splatting (3DGS) framework tailored to sparse-view industrial reconstruction was introduced, enabling robust reconstruction from limited viewpoints and offers a practical geometric foundation for automated inspection and remote equipment monitoring.
A comprehensive geometric-semantic fusion mechanism that resolves geometric noise and semantic ambiguity by explicitly utilizing semantic guidance and formulating 3D segmentation as solving point-and-set merging and partitioning problems, and an innovative manifold-distance-based point cloud refinement strategy.
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