UAVFF3D introduces a geometry-aware real-synthetic benchmark and evaluation protocol for feed-forward UAV 3D reconstruction that supports domain adaptation and reduces errors in camera pose and scene geometry.
ACM Transactions on Graphics (ToG) , volume=
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AmbiSuR adds intrinsic photometric disambiguation and a self-indication module to Gaussian Splatting to resolve ambiguities and improve surface reconstruction accuracy.
LeGS turns density control in 3D Gaussian Splatting into a learnable RL policy whose reward is derived from a closed-form sensitivity analysis that measures each Gaussian's marginal contribution to reconstruction quality.
ReorgGS reorganizes the Gaussian distribution in converged 3DGS models by resampling centers and covariances to reduce parameterization degeneration and enable better subsequent optimization.
citing papers explorer
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UAVFF3D: A Geometry-Aware Benchmark for Feed-Forward UAV 3D Reconstruction
UAVFF3D introduces a geometry-aware real-synthetic benchmark and evaluation protocol for feed-forward UAV 3D reconstruction that supports domain adaptation and reduces errors in camera pose and scene geometry.
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Revisiting Photometric Ambiguity for Accurate Gaussian-Splatting Surface Reconstruction
AmbiSuR adds intrinsic photometric disambiguation and a self-indication module to Gaussian Splatting to resolve ambiguities and improve surface reconstruction accuracy.
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Beyond Heuristics: Learnable Density Control for 3D Gaussian Splatting
LeGS turns density control in 3D Gaussian Splatting into a learnable RL policy whose reward is derived from a closed-form sensitivity analysis that measures each Gaussian's marginal contribution to reconstruction quality.
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ReorgGS: Equivalent Distribution Reorganization for 3D Gaussian Splatting
ReorgGS reorganizes the Gaussian distribution in converged 3DGS models by resampling centers and covariances to reduce parameterization degeneration and enable better subsequent optimization.