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arXiv · 2603.07587

3DGS-HPC: Distractor-free 3D Gaussian Splatting with Hybrid Patch-wise Classification

Jiahao Chen·Yipeng Qin·Ganlong Zhao·Xin Li·Wenping Wang·Guanbin Li
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Abstract

3D Gaussian Splatting (3DGS) has demonstrated remarkable performance in novel view synthesis and 3D scene reconstruction, but its quality often degrades in real-world environments due to transient distractors, such as moving objects and varying shadows. Existing methods commonly introduce semantic priors from pre-trained vision models either to group pixels into coherent regions or to define perceptual error metrics. However, semantic grouping is often misaligned with the binary static/transient distinction, while perceptual features can be fragile under appearance perturbations introduced during 3DGS optimization. We propose 3DGS-HPC, a framework that addresses these issues by combining two complementary principles: a patch-wise classification strategy that leverages local spatial consistency for robust region-level decisions, and a hybrid classification metric that adaptively integrates photometric and perceptual cues for more reliable separation. Extensive experiments demonstrate the superiority and robustness of our method in mitigating distractors to improve 3DGS-based novel view synthesis. Our project page is https://cnhaox.github.io/3DGS-HPC/ .

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Jiahao Chen, Yipeng Qin, Ganlong Zhao, Xin Li, Wenping Wang, Guanbin Li. 2026-03-08. 3DGS-HPC: Distractor-free 3D Gaussian Splatting with Hybrid Patch-wise Classification. https://arxiv.org/abs/2603.07587

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