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Adaptive-scale color offset and error-guided Gaussian reallocation for multi-scale 3DGS improves multi-scale rendering quality across rendering scales on Mip-NeRF 360, Tanks and Temples, and Deep Blending, with larger gains at coarse scales.
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Multi-scale rendering exposes scale-dependent reconstruction errors in 3D Gaussian splatting (3DGS) when the rendering resolution differs from the training resolution. Existing anti-aliasing and multi-scale optimization methods reduce these artifacts, but each Gaussian usually keeps a fixed color representation and a fixed spatial allocation after densification. This fixed representation is restrictive at coarse scales because one rendered pixel combines multiple Gaussian contributions into an average color that can differ from the color assigned to each Gaussian. In addition, Gaussians allocated by fine-scale reconstruction criteria may provide insufficient coverage for regions with persistent coarse-scale errors. This paper proposes adaptive-scale color offset and error-guided Gaussian reallocation for multi-scale 3DGS. The adaptive-scale color offset applies a scale-specific correction to the shared base color of each Gaussian while keeping its geometry unchanged. Error-guided Gaussian reallocation shifts low-contribution Gaussians toward high-error regions under a fixed primitive budget and preserves Gaussians that support coarse-scale rendering. Experimental results show that the proposed method improves multi-scale rendering quality across rendering scales on Mip-NeRF 360, Tanks and Temples, and Deep Blending, with larger gains at coarse scales. These results demonstrate that adapting Gaussian color and primitive allocation together improves scale-consistent 3DGS rendering without modifying the inference-stage rendering process.
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@article{Park2026Adaptive,
title = {Adaptive-Scale Color Offset and Error-Guided Gaussian Reallocation for Multi-Scale 3D Gaussian Splatting},
author = {Hyeonbin Park and Yooho Lee and Dongho Lee and Dongsan Jun},
journal = {Mathematics},
year = {2026},
doi = {10.3390/math14152713},
url = {https://doi.org/10.3390/math14152713}
}
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