Distilled-3DGS:Distilled 3D Gaussian Splatting
3D Gaussian Splatting (3DGS) has exhibited remarkable efficacy in novel view
synthesis (NVS). However, it suffers from a significant drawback: achieving
high-fidelity rendering typically necessitates a large number of 3D Gaussians,
resulting in substantial memory consumption and storage requirements. To
address this challenge, we propose the first knowledge distillation framework
for 3DGS, featuring various teacher models, including vanilla 3DGS,
noise-augmented variants, and dropout-regularized versions. The outputs of
these teachers are aggregated to guide the optimization of a lightweight
student model. To distill the hidden geometric structure, we propose a
structural similarity loss to boost the consistency of spatial geometric
distributions between the student and teacher model. Through comprehensive
quantitative and qualitative evaluations across diverse datasets, the proposed
Distilled-3DGS, a simple yet effective framework without bells and whistles,
achieves promising rendering results in both rendering quality and storage
efficiency compared to state-of-the-art methods. Project page:
https://distilled3dgs.github.io . Code:
https://github.com/lt-xiang/Distilled-3DGS .
68ac8ad486b21a0e2e358c90
Lintao Xiang
68ac8ad486b21a0e2e358c91
Xinkai Chen
68ac8ad486b21a0e2e358c92
Jianhuang Lai
68ac8ad486b21a0e2e358c93
Guangcong Wang
admin_assigned
2025-08-25T20:34:22.508Z
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Guangcong Wang
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GuangcongWang
Distilled-3DGS:Distilled 3D Gaussian Splatting
3D Gaussian Splatting (3DGS) has exhibited remarkable efficacy in novel view
synthesis (NVS). However, it suffers from a significant drawback: achieving
high-fidelity rendering typically necessitates a large number of 3D Gaussians,
resulting in substantial memory consumption and storage requirements. To
address this challenge, we propose the first knowledge distillation framework
for 3DGS, featuring various teacher models, including vanilla 3DGS,
noise-augmented variants, and dropout-regularized versions. The outputs of
these teachers are aggregated to guide the optimization of a lightweight
student model. To distill the hidden geometric structure, we propose a
structural similarity loss to boost the consistency of spatial geometric
distributions between the student and teacher model. Through comprehensive
quantitative and qualitative evaluations across diverse datasets, the proposed
Distilled-3DGS, a simple yet effective framework without bells and whistles,
achieves promising rendering results in both rendering quality and storage
efficiency compared to state-of-the-art methods. Project page:
https://distilled3dgs.github.io . Code:
https://github.com/lt-xiang/Distilled-3DGS .
A knowledge distillation framework for 3D Gaussian Splatting improves rendering quality and storage efficiency by aggregating outputs from multiple teacher models to optimize a lightweight student model.
3D Gaussian Splatting novel view synthesis knowledge distillation structural similarity loss spatial geometric distributions