DART-3DGS: Distance-Aware Replication and Age-Gated Densification for 3D Gaussian Splatting

3D Gaussian Splatting (3DGS) renders novel views in real time, but quality is limited by two coupled factors: a Structure-from-Motion (SfM) seed that is thin in scene peripheries and weakly textured regions, and an adaptive density-control rule that lets new Gaussians split repeatedly, inflating primitive count, memory, and training time. We address both with two backbone-agnostic add-ons that leave the rasterizer and pruning untouched. Distance-Aware Replication on Tangent-planes (DART) enriches the seed at initialization, replicating each SfM point on its tangent plane within a distance-aware radius, and adding coverage in the scene periphery, where the SfM cloud is typically sparsest, without depth or external cues. Age-Gated Eligibility (AGE) bounds the budget by letting a Gaussian split or clone only after reaching a minimum age, turning the splitting cascade into controlled refinement. Applied unchanged to vanilla 3DGS and anchor-based Scaffold-GS on three standard benchmarks, both add-ons improve both backbones: the Scaffold-GS variant is strongest on six of nine dataset–metric combinations and second on the rest, and under a second protocol leads all three Tanks & Temples metrics and both Deep Blending SSIM/LPIPS, with PSNR within 0.5% of the top result. Gains concentrate on sparsely seeded scenes, while AGE bounds the added cost, lowering primitive count, memory, and training time relative to DART alone at unchanged quality.

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Publication Details

Journal
Journal of Imaging
Published
2026-10-04
DOI
https://doi.org/10.3390/jimaging12100483
Primary Topic
3D Shape Modeling and Analysis
Type
article
Field-Weighted Citation Impact
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article

DART-3DGS: Distance-Aware Replication and Age-Gated Densification for 3D Gaussian Splatting

Mohannad A. M. Al-Ja’afari, Firas Abedi, You Yang, Qiong Liu
Journal of Imaging
3D Shape Modeling and Analysis
article

DART-3DGS: Distance-Aware Replication and Age-Gated Densification for 3D Gaussian Splatting

Mohannad A. M. Al-Ja’afari, Firas Abedi, You Yang, Qiong Liu
article en

Abstract

3D Gaussian Splatting (3DGS) renders novel views in real time, but quality is limited by two coupled factors: a Structure-from-Motion (SfM) seed that is thin in scene peripheries and weakly textured regions, and an adaptive density-control rule that lets new Gaussians split repeatedly, inflating primitive count, memory, and training time. We address both with two backbone-agnostic add-ons that leave the rasterizer and pruning untouched. Distance-Aware Replication on Tangent-planes (DART) enriches the seed at initialization, replicating each SfM point on its tangent plane within a distance-aware radius, and adding coverage in the scene periphery, where the SfM cloud is typically sparsest, without depth or external cues. Age-Gated Eligibility (AGE) bounds the budget by letting a Gaussian split or clone only after reaching a minimum age, turning the splitting cascade into controlled refinement. Applied unchanged to vanilla 3DGS and anchor-based Scaffold-GS on three standard benchmarks, both add-ons improve both backbones: the Scaffold-GS variant is strongest on six of nine dataset–metric combinations and second on the rest, and under a second protocol leads all three Tanks & Temples metrics and both Deep Blending SSIM/LPIPS, with PSNR within 0.5% of the top result. Gains concentrate on sparsely seeded scenes, while AGE bounds the added cost, lowering primitive count, memory, and training time relative to DART alone at unchanged quality.

Journal of ImagingVol. 12(10)
Wuhan National Laboratory for Optoelectronics (CN), Al-Furat Al-Awsat Technical University (IQ), Huazhong University of Science and Technology (CN)
Openalex Percentile: Top 17%
3D Shape Modeling and Analysis
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