From Coarse to Fine: Learnable Discrete Wavelet Transforms for Efficient 3D Gaussian Splatting

• URL: http://arxiv.org/abs/2506.23042v1
• Date: Sun, 29 Jun 2025 00:27:17 GMT
• Title: From Coarse to Fine: Learnable Discrete Wavelet Transforms for Efficient 3D Gaussian Splatting
• Authors: Hung Nguyen, An Le, Runfa Li, Truong Nguyen,
• Abstract summary: AutoOpti3DGS is a training-time framework that automatically restrains Gaussian proliferation without sacrificing visual fidelity.<n>Wavelet-driven, coarse-to-fine process delays the formation of redundant fine Gaussians.
• Score: 5.026688852582894
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 3D Gaussian Splatting has emerged as a powerful approach in novel view synthesis, delivering rapid training and rendering but at the cost of an ever-growing set of Gaussian primitives that strains memory and bandwidth. We introduce AutoOpti3DGS, a training-time framework that automatically restrains Gaussian proliferation without sacrificing visual fidelity. The key idea is to feed the input images to a sequence of learnable Forward and Inverse Discrete Wavelet Transforms, where low-pass filters are kept fixed, high-pass filters are learnable and initialized to zero, and an auxiliary orthogonality loss gradually activates fine frequencies. This wavelet-driven, coarse-to-fine process delays the formation of redundant fine Gaussians, allowing 3DGS to capture global structure first and refine detail only when necessary. Through extensive experiments, AutoOpti3DGS requires just a single filter learning-rate hyper-parameter, integrates seamlessly with existing efficient 3DGS frameworks, and consistently produces sparser scene representations more compatible with memory or storage-constrained hardware.

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