UltraGS Frameworks: Advanced Gaussian Splatting

• UltraGS Frameworks are advanced, modular systems that generalize Gaussian Splatting for high-fidelity 3D/4D volumetric rendering and compression.
• They employ compositional architectures with replaceable modules such as RepresentationBase, CompressorBase, and RendererBase to facilitate rapid research integration and benchmarking.
• Integrating differentiable rasterization, GPU optimizations, and rate–distortion techniques, UltraGS frameworks achieve state-of-the-art photorealistic and multimodal rendering.

UltraGS Frameworks are advanced, modular systems that generalize 3D (and 4D) Gaussian Splatting to high-fidelity, efficient, and often physics-informed volumetric rendering pipelines. Their design fuses explicit scene representations with optimization, compression, hybrid modeling, and task-specific extensions, enabling photorealistic rendering, compact streaming, and adaptation to diverse application domains such as immersive media and medical imaging. UltraGS frameworks combine differentiable, compositional architectures with GPU accelerability and state-of-the-art memory and bandwidth optimizations.

1. Unified Architecture and Modularity

UltraGS frameworks follow a highly compositional and modular design that abstracts each computational stage into replaceable, pluggable components. Typical architectures, exemplified by GSCodec Studio and GauStudio, are structured into sequential stages for data ingestion, Gaussian initialization, joint parameter optimization, scene enhancement (densification, hybridization), compression, and real-time rendering (Li et al., 2 Jun 2025, Ye et al., 2024). Each stage is accessible via a core class interface—RepresentationBase, CompressorBase, RendererBase—with extensibility via registration decorators or factory methods.

UltraGS frameworks natively support both static 3D scenes and dynamic 4D content. Dynamic scenarios employ either per-splat polynomial trajectory models, global motion basis expansions, or Gaussian kernels over time for attribute sharing and memory efficiency (Li et al., 2 Jun 2025). This modularity facilitates benchmarking, integration of emerging research, and rapid algorithmic replacement or extension.

2. Gaussian Splat Scene Representations

All UltraGS systems are based on explicit, anisotropic Gaussian primitives ("splats") parameterized by position, covariance (scale/anisotropy), color or multi-modal attribute (typically via low-order spherical harmonics or learned features), and opacity. A scene is modeled as a set of $N$ such splats:

$$f(x) = \sum_{i=1}^N w_i \cdot \mathcal{N}(x; p_i, \Sigma_i)$$

For static 3D scenes, storage can involve either SH color coefficients or learned feature vectors, optionally followed by an MLP mapping in image space (Li et al., 2 Jun 2025, Ye et al., 2024). Dynamic scenes use parameterized trajectories in $SE(3)$ or learned motion bases to describe Gaussian evolution over time (Li et al., 2 Jun 2025).

Hybrid scene decompositions, such as foreground–background separation via "skyball" Gaussians on the unit sphere, address unbounded or outdoor cases and suppress artifacts by blending foreground splats with spherical background models (Ye et al., 2024).

3. Physics-Aware Adaptations and Multimodal Extensions

UltraGS frameworks depart from pure geometric splatting in application-specific domains. In ultrasound—via UltraGauss and UltraGS—splatting is adapted for wave-physics:

• Probe-plane intersection replaces conventional ray projection, yielding 3D Mahalanobis kernels on the probe’s acquisition geometry. The per-pixel opacity is:

$$\hat\alpha_i(x) = \alpha_i \exp\left[ -\frac{1}{2} (x_0 - \mu_i^{3D})^\top (\Sigma_i^{3D})^{-1} (x_0 - \mu_i^{3D}) \right]$$

where $x_0$ is the probe-plane coordinate (Eid et al., 8 May 2025).

• Rendering incorporates ultrasound-specific radiance: low-order SH with learnable field-of-view, Beer-Lambert depth attenuation, specular/quadratic reflection, and cross-channel scattering (Yang et al., 11 Nov 2025).

Other frameworks, such as UniGS, incorporate semantic logits, depth, and analytic normal gradients for true multimodal rendering. Per-Gaussian storage expands to include explicit rotation (quaternion), scale, and auxiliary attributes for unified geometric consistency across modalities (Xie et al., 14 Oct 2025).

4. Compression, Pruning, and Rate–Distortion Optimization

UltraGS systems integrate sophisticated compression strategies at both training and post-training phases. Training-time simulation injects differentiable quantization noise or straight-through estimation, with explicit entropy terms in the loss:

$$\mathcal{L} = \mathcal{L}_{\mathrm{dist}}(I_{\mathrm{rendered}}, I_{\mathrm{gt}}) + \lambda \mathcal{L}_{\mathrm{rate}}$$

Compression includes learnable or empirical pruning (via masks or thresholding), 3D-to-2D splat clustering (e.g., PLAS sorting), quantization (variable bitwidth), and final lossless or entropy-coded packaging (e.g., PNG/HEIF, ANS). Key best practices include:

• Uniform-noise quantization yielding superior rate–distortion compared to STE.
• Factorized density entropy models producing more compact parameter sets than per-point Gaussian predictors.
• 8-bit quantization as an accuracy–compression sweet spot; lower bitwidths incur visual quality degradation (Li et al., 2 Jun 2025).

Pruning can be made fully differentiable by appending a learned scalar attribute to each splat, with loss $\mathcal{L}_K$ penalizing deviations from the participation threshold and automated splat removal (Xie et al., 14 Oct 2025).

5. Differentiable Rasterization and Analytic Gradients

UltraGS frameworks emphasize efficient, differentiable GPU rasterization. CUDA-accelerated tile-based splatting, analytic ray–ellipsoid (or disk) intersection for depth, and closed-form surface normal estimation via finite differences over the rendered depth map are core features (Xie et al., 14 Oct 2025, Ye et al., 2024). Covariance matrices are parameterized for positive-definiteness (e.g., Cholesky-style) and numerical stability (Eid et al., 8 May 2025). Analytic gradients of depth and normals are derived for fast, geometry-aware convergence, propagating through transformations and attributes (e.g., via chain rule for $t_{\rm mid}$ in ellipsoid intersection).

GPU optimizations—including boundary culling, stream compaction, and memory coalescence—enable scenes with $10^6$–$10^7$ splats to be rendered or trained in real-time.

6. Evaluation, Applications, and Limitations

UltraGS frameworks demonstrate state-of-the-art photorealistic or multimodal scene synthesis. Rate–distortion curves (PSNR, SSIM, LPIPS) are reported for standard 3D datasets (Tanks & Temples, MipNeRF360), 4D dynamic scenes, and in medical applications, volumetric ultrasound (UltraGS, UltraGauss) achieves SSIM up to 0.99 and real-time (<10 min) reconstruction on clinical data (Eid et al., 8 May 2025, Yang et al., 11 Nov 2025). Clinical expert surveys confirm superior anatomical realism relative to voxel or implicit field baselines.

Limitations commonly include position data consuming >40% of bitrate (suggesting future work on coordinate compression), the need for non-uniform or learned quantization per attribute, and the potential gains from advanced entropy modeling (normalizing flows, hyperpriors) (Li et al., 2 Jun 2025). In dynamic scenes, supporting inter-group-of-frame prediction (P-frames, B-frames) and region-of-interest streaming remain open extensions.

7. System Integration and Research Platform

UltraGS systems are released as open-source, production-scale platforms (e.g., GSCodec Studio, UltraGauss, UniGS, GauStudio). Researchers can:

• Instantiate new scene representations by subclassing or registering RepresentationBase derivatives.
• Integrate custom compressors or quantizers, add post-processing pipelines, or extend streaming/rendering targets with minimal refactoring.
• Mix-and-match standardized modules for benchmarking, ablation, and deployment in varied immersive media, medical imaging, or semantic reconstruction tasks.

As a result, UltraGS frameworks unify reconstruction, compression, and rendering in a compositional pipeline, enabling rapid research iteration and deployment of state-of-the-art, high-fidelity Gaussian Splatting across applications (Li et al., 2 Jun 2025, Xie et al., 14 Oct 2025, Ye et al., 2024, Yang et al., 11 Nov 2025, Eid et al., 8 May 2025).