RayGaussX: Efficient Gaussian Volumetric Rendering

• RayGaussX is a ray-marching and volumetric rendering framework that leverages irregular Gaussian primitives for high-fidelity, real-time novel view synthesis.
• It employs innovative techniques such as empty-space skipping, adaptive sampling, and perspective-aware densification to achieve up to 80x faster rendering and improved image quality.
• By utilizing BVH acceleration and spatial reordering for enhanced memory and execution coherence, RayGaussX delivers significant speedups and robust performance on large-scale, real-world scenes.

RayGaussX is a ray-marching and volumetric rendering framework that advances the efficiency, scalability, and quality of Gaussian-based scene representations for real-time and high-fidelity novel view synthesis. Extending the original RayGauss architecture, which models radiance and density fields with irregular elliptical basis functions rendered via BVH-accelerated volume ray casting, RayGaussX systematically overcomes computational bottlenecks that previously inhibited real-time performance on real-world and large-scale scenes. The system's design introduces a synergistic set of algorithmic innovations—comprising volumetric acceleration strategies, improved memory and execution coherence, regularization for geometric stability, and densification adapted to perspective effects—that jointly deliver an improvement of up to 5x–12x in training speed and 50x–80x in rendering speed, while also securing measurable gains in graphical quality (notably up to +0.56 dB in PSNR on complex benchmarks) (Blanc et al., 9 Sep 2025).

1. Algorithmic Innovations in RayGaussX

RayGaussX incorporates multiple mutually reinforcing innovations to address the inefficiencies of prior Gaussian ray-marching frameworks:

• Empty-space skipping and adaptive sampling: By leveraging the BVH (Bounding Volume Hierarchy) representation of irregular, anisotropic Gaussian primitives, RayGaussX employs a two-phase shader strategy—using an "AnyHit" shader to rapidly skip ray intervals with no density, coupled with a "ClosestHit" shader for intervals intersecting Gaussians. This ensures sample evaluations are performed only in regions contributing to the rendered color, drastically reducing superfluous computation.
• Adaptive step size modulation: Rather than traversing ray segments with a fixed ∆S, RayGaussX dynamically modulates segment length as a function of camera distance and the cumulative transmittance. Formally,

$$\Delta S_i = N_s \cdot \min\left\{ \max\left(\frac{d_i}{\beta}, \Delta t_{\min}\right), \Delta t_{\max} \right\} \cdot T_i^{-1/3}$$

where $d_i$ is the distance from the camera, and $T_i$ is current transmittance, permitting aggressive coarsening of the sample density in low-contribution regions.

• Ray and primitive coherence via spatial reordering: RayGaussX enforces memory and execution coherency by grouping rays (e.g., by spatial tiling) and reordering Gaussian primitives using Morton (Z-order) curves to achieve data locality. This process minimizes warp divergence on GPU architectures, improving memory bandwidth utilization and parallel throughput.
• Scale regularization (isotropic loss): To curb inefficiencies from highly anisotropic Gaussians (whose AABB volumes far exceed their true ellipsoid support, leading to excessive false-positive intersections), RayGaussX introduces an explicit loss:

$$\mathcal{L}_s = \frac{1}{N} \sum_{l}( \max(r_{\max,l}, r_0) - r_0 )$$

Here $r_{\max,l}$ is an upper bound on the support radius induced by the Gaussian's scale parameters, penalizing pathological parameterizations during optimization.

• Revised densification criterion for perspective robustness: Classic RayGauss used the average norm of the gradient with respect to the Gaussian mean in world coordinates to trigger split operations for under-dense regions:

$$\frac{1}{I_l} \sum_{i} \|\nabla_{\mu_l} L_i\| > \tau$$

but this under-represents distant Gaussians due to the diminishing gradient magnitude with increasing scene depth. RayGaussX introduces a corrective factor $\alpha_i = \frac{\|\mu_l - o_i\|}{f}$ (camera origin $o_i$, focal length $f$), yielding

$$\frac{1}{I_l} \sum_{i} \alpha_i \|\nabla_{\mu_l} L_i\| > \tau$$

ensuring fair densification and superior coverage in distant regions, directly boosting scene completeness and image-space detail at depth.

2. Volumetric Rendering Acceleration Strategies

The fundamental performance improvement in RayGaussX stems from the integration of classical volumetric acceleration techniques within the BVH-based volumetric ray marching pipeline:

• Empty-space skipping rapidly eliminates sampling along ray segments lacking any nonzero density by pre-querying the BVH. The method checks for overlaps over fixed intervals, skipping intervals entirely if no intersection is detected—a strategy long-used in grid-based volumetric renderers, but here generalized for irregular, learned Gaussian primitives and nonuniform support.
• Adaptive sampling ensures sample budgets are concentrated in high-transparency, fine-detail regions, especially near the camera or where light transport is most sensitive to density variations. Far-field and high-attenuation zones are traversed with larger step sizes, leading to order-of-magnitude reductions in overall sample count per ray.

These strategies together yield, in benchmark experiments, FPS improvements from sub-interactive (e.g., 0.5 FPS in RayGauss) to real-time performance (e.g., 27 FPS with all accelerations enabled on a 1297×840 pixel scene).

3. Ray Coherence, Memory Access, and Parallel Scalability

RayGaussX achieves substantial scaling through improved ray and data coherency:

• Ray reordering: By grouping rays that traverse similar regions and reordering their execution, the framework aligns per-warp execution on the GPU—reducing conditional divergence and improving utilization of the cache and memory subsystems.
• Primitive reordering (Z-curve/Morton order): An analogous reordering of the Gaussian primitive buffer ensures spatially local primitives are adjacent in memory, increasing the efficiency of BVH traversal and intersection checks, particularly for rays within the same spatial tile.
• These techniques are crucial for exploiting manycore architectures, underlining RayGaussX’s focus on not only algorithmic, but also systems-level scalability for large, real-world scenes.

4. Gaussian Scale Regularization and Scene Completeness

Highly anisotropic Gaussians, if left unregularized, inflate the bounding boxes seen by the BVH, causing rampant false-positive intersection checks during ray marching. This inflates computational load and can degrade image quality by polluting color/opacity accumulation in the transmittance estimator. RayGaussX’s isotropic loss penalizes such configurations, enforcing that each Gaussian’s axis-aligned support does not vastly exceed its effective ellipsoid support, ultimately reducing unnecessary intersection tests and confining computational resources to visually relevant primitives.

The improved densification scheme further ensures that scene completeness is maintained—a crucial requirement in novel view synthesis for outdoor or large-scale environments (e.g., Tanks & Temples), where compact, distant structures would otherwise be underrepresented leading to visible “holes” in rendered images (Blanc et al., 9 Sep 2025).

5. Numerical Performance and Qualitative Results

Empirical results on both synthetic (e.g., NeRF Synthetic) and real-world benchmarks (Mip-NeRF360, Tanks & Temples) demonstrate:

• PSNR improvement: Up to +0.56 dB for difficult real-world scenes.
• Training time reduction: From 33 minutes (RayGauss) to 22 minutes on NeRF Synthetic; up to 5x–12x reduction on large-scale benchmarks.
• Rendering speed: Increases from 25 FPS (RayGauss) to 85 FPS (RayGaussX), with further improvements on more complex real-world scenes (e.g., 0.5 FPS → 27 FPS after all ablations).
• Image-space detail and completeness: The combination of regularization and perspective-aware densification yields more accurate and continuous rendering results, particularly in regions at scene depth, with fewer artifacts due to density under- or over-representation.

6. Applications and Future Impact

RayGaussX’s advancements position the framework for wide deployment in settings requiring real-time, high-fidelity synthesis of novel views over large-scale, real-world datasets:

• Interactive and immersive systems: The achieved real-time performance enables its use in VR/AR, remote telepresence, and mixed-reality content creation where latency and visual fidelity are critical.
• Outdoor and complex environments: Robust handling of distant scene content extends Gaussian-based approaches beyond indoor or medium-scale environments to city-scale or landscape scenes, with applications in virtual tourism and remote navigation.
• Open-source ecosystem: The release of source code, videos, and supplementary materials facilitates reproducibility and incorporation into downstream research and production pipelines, accelerating community adoption.

7. Project Resources

RayGaussX’s codebase and demonstration materials are hosted at:

• Project page and demos: https://raygaussx.github.io/
• GitHub repository: https://github.com/hugobl1/raygaussx

These resources allow researchers to paper, replicate, or extend the system for specialized use cases.

---

In summary, RayGaussX systematically advances Gaussian-based volumetric rendering by combining algorithmic acceleration (empty-space skipping, adaptive sampling), systems-level improvements (ray and data locality), novel geometric regularization, and perspective-aware densification. This results in a rendering framework that not only achieves state-of-the-art quality for novel view synthesis but, crucially, does so at real-time speeds suitable for high-resolution, real-world scenarios (Blanc et al., 9 Sep 2025).