3DGS Self-Initialization in Radiance Fields

• The paper introduces RAIN-GS, a method leveraging self-initialization in 3DGS to overcome SfM limitations using frequency-domain analysis and adaptive variance scheduling.
• It employs a sparse-large-variance initialization that emphasizes global, low-frequency scene structures before refining fine details with progressive low-pass filtering.
• Experimental evaluations demonstrate that this approach improves PSNR, SSIM, and LPIPS metrics, enabling more robust and flexible real-time scene reconstructions.

3D Gaussian Splatting (3DGS) Self-initialization denotes a family of strategies that enable the training of explicit radiance field models without reliance on highly accurate, precomputed point cloud initializations, such as those obtained from Structure-from-Motion (SfM) or Multi-View Stereo (MVS) pipelines. Self-initialization has become a critical design consideration as these radiance field models are deployed in scenarios where feature-rich scenes or calibrated camera poses are unavailable. Recent works scrutinize the failure modes of point-based initializations and propose novel schemes driven by frequency analysis, spatial variance scheduling, attention mechanisms, learned network priors, and data-driven regularization, thereby extending 3DGS to previously inaccessible domains.

1. Traditional SfM Initialization and its Limitations

The prevailing 3DGS pipeline traditionally seeds Gaussian primitives using a sparse point cloud derived from SfM outputs. These points encapsulate coarse scene approximation, predominantly low-frequency geometric and photometric information, which bootstraps subsequent fine-grained optimization. However, degradation in point cloud quality—due to noise, sparsity, textureless regions, or view constraints—leads to severe reconstruction artifacts and a characteristic 4–5 dB loss in PSNR (Jung et al., 14 Mar 2024). Dense random initialization, where Gaussians are seeded indiscriminately with small variance, causes overfitting to high-frequency details early in training and instability in surface formation. These observations, confirmed via frequency-domain analysis, motivate the strict separation of coarse and fine optimization stages that classical SfM-based 3DGS lacks when initialization is poor.

2. Frequency-domain Analysis and Sparse-Large-Variance (SLV) Initialization

The spectrum of initialized reconstructions reveals that an SfM-derived point cloud intrinsically encodes the scene’s low-frequency information, acting as a natural frequency regularizer. This property has been quantified by extracting image rows and subjecting them to Fourier analysis: SfM-initialized 3DGS reconstructions exhibit strong low-frequency components but limited high-frequency energy (Jung et al., 14 Mar 2024). Starting from dense random initialization or small-variance regimes rapidly introduces high-frequency components, resulting in artifacts. Accordingly, the RAIN-GS approach introduces SLV initialization, where a small number of Gaussians ($N \sim 10$) are randomly distributed within an expanded scene volume, and the covariance of each is determined by its nearest neighbors. Mathematically, this produces large initial variances, ensuring initial splats cover broad regions. Optimizers therefore privilege global (low-frequency) structure before progressively resolving finer details.

3. Progressive Gaussian Low-pass Filtering and Optimization Scheme

RAIN-GS further proposes adaptive low-pass control in the splatting process via dynamic adjustment of the diagonal additive $s$ to the covariance matrix. Instead of a static value, $s$ varies as:

$$s = \min\left( \max\left( \frac{HW}{9\pi N}, 0.3 \right), 300.0 \right)$$

where $H$ and $W$ are image dimensions and $N$ is the Gaussian count. This enforces initial blurring, delaying the optimizer’s access to high-frequency details until later epochs. The reconstructed 2D Gaussian splat is thus

$$G_i'(x) = \exp\left( -\frac{1}{2} (x - \mu_i')^T (\Sigma_i' + sI)^{-1} (x - \mu_i') \right)$$

with $s$ progressively annealed as optimization proceeds (Jung et al., 14 Mar 2024). The result is stable, coarse-to-fine learning dynamics from self-initialized random Gaussians, yielding reconstructions statistically equivalent or superior to classical SfM-seeded 3DGS.

4. Quantitative and Qualitative Performance Evaluation

Experiments demonstrate that RAIN-GS, using sparse random initialization and progressive low-pass filtering, closes the performance gap attributed to inaccurate initialization. For Mip-NeRF360, RAIN-GS with random point clouds improved PSNR from 21.0 dB (dense random DSV) to 23.7 dB (SLV), with corresponding gains in SSIM and LPIPS. In qualitative benchmarks (e.g., Tanks-and-Temples, Deep Blending), reconstructions exhibit fewer high-frequency artifacts and more accurate depth. RAIN-GS not only matches but sometimes surpasses traditional 3DGS initialized with high-quality SfM clouds when evaluated on standard metrics and visual fidelity (Jung et al., 14 Mar 2024).

5. Practical Implications and Extensions

The robust self-initialization pipeline opens 3DGS to applications previously precluded by SfM limitations, such as scenes exhibiting symmetry, strong specularities, or low texture. It also accomodates external camera pose providers, such as AR devices, bypassing the computational overhead of SfM. The reduction in Gaussian count and memory load during early training suggests further possibilities for real-time deployment and efficient optimization schedules. A plausible implication is that self-initialization strategies—when combined with hybrid supervisory signals (depth maps, error regions)—may extend to non-Gaussian radiance field representations and additional modalities. Here, analysis in the frequency domain and adaptive variance scheduling provide generalizable frameworks for initialization-free optimization across explicit scene representations.

6. Limitations and Future Directions

While RAIN-GS and related self-initialization schemes substantially relax dependency on SfM, current approaches assume access to sufficiently broad photometric supervision and struggle in cases of organizational occlusion, extreme noise, or missing modalities. Progress may be realized by integrating depth-aware regularization, spatial priors, or active error maps, guiding the transition from low-frequency bootstrapping to high-frequency detail learning (Jung et al., 14 Mar 2024). Further, quantifying resource savings and real-time constraints in large-scale, interactive systems remains an active area of paper.

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In summary, 3DGS self-initialization represents a principled break from reliance on accurate SfM, driven by frequency-domain analysis and adaptive variance scheduling. The RAIN-GS framework demonstrates that a sparse-large-variance initialization coupled with progressively scheduled low-pass filtering yields stable and high-quality reconstructions from random point clouds, setting a new standard for flexibility and robustness in real-time novel view synthesis.