Real-Time Radiance Field Rendering

• Real-time radiance field rendering is a technique for synthesizing photorealistic 3D views using neural or hybrid representations at interactive speeds.
• It accelerates the classic NeRF model by leveraging explicit scene tabulation, sparse data structures, and optimized hardware-software co-design.
• The approaches enable advanced features like dynamic scene handling, HDR imaging, and cinematic effects, vital for immersive AR/VR experiences.

Real-time radiance field rendering refers to methods for synthesizing photorealistic novel views of complex 3D scenes at interactive frame rates using neural or hybrid representations. This area originated with implicit volumetric models such as Neural Radiance Fields (NeRF), which encode a scene as a function mapping 3D location and view direction to radiance and opacity, enabling free-viewpoint synthesis from sparse input images. However, classic NeRF implementations require hundreds of thousands of neural network queries per ray, resulting in seconds or minutes per frame even on modern GPUs, precluding real-time applications. Recent years have seen the development of a spectrum of techniques—encompassing architectural reformulation, sparse explicit data structures, hardware co-design, and hybrid rasterization approaches—that achieve orders-of-magnitude speedup while preserving or even enhancing visual fidelity. This domain is foundational for emerging applications in interactive 3D graphics, virtual/augmented reality, and light field displays.

1. Principles of Neural Radiance Fields and Computational Bottlenecks

Neural Radiance Fields encode scenes as a continuous function $f(\mathbf{x}, \mathbf{d})$, typically parameterized by a multilayer perceptron (MLP), that returns density $\sigma$ and color $\mathbf{c}$ for any spatial coordinate $\mathbf{x} \in \mathbb{R}^3$ and view direction $\mathbf{d} \in \mathbb{S}^2$. Synthesizing the color for a pixel involves numerically integrating this field along each camera ray:

$$C(\mathbf{r}) = \sum_{i} T_i (1 - \exp(-\sigma_i \delta_i)) \mathbf{c}_i, \quad T_i = \exp\left(-\sum_{j<i} \sigma_j \delta_j\right)$$

where $\delta_i$ denotes sample spacing. This computation is repeated for millions of samples per image, resulting in prohibitive computational expense. Major bottlenecks include:

• Repeated MLP evaluations at every sampled 3D position and view direction;
• The need for dense sampling along rays due to the inability to predict scene geometry in early iterations;
• High memory and bandwidth usage for storing and querying model parameters and sample data.

Overcoming these limitations is the core focus of real-time radiance field rendering.

2. Explicit Scene Tabulation: Octree and Voxel-Based Representations

Pre-tabulating learned radiance fields onto sparse spatial data structures is a primary strategy for acceleration. The PlenOctree framework (Yu et al., 2021) first modifies NeRF to predict spherical harmonic (SH) coefficients per spatial cell—decoupling view direction from network inference—and then densely samples the trained field to populate an octree structure. Each octree leaf stores precomputed density and SH attributes, allowing for rapid ray marching:

• The radiance along each ray is accumulated by traversing only the occupied voxels, leveraging view-dependent color via SH basis expansion.
• Fine details and view-dependent effects (e.g., specularities) are preserved, as the SH coefficients allow reconstruction of color from any direction without further network queries.

This approach achieves over 150 FPS at 800×800 resolution (a speedup >3000× over original NeRF), and the octree can be directly fine-tuned using differentiable volume rendering for further quality optimization.

Similarly, the Sparse Neural Radiance Grid (SNeRG) (Hedman et al., 2021) bakes the NeRF into a block-sparse voxel grid, storing color, density, and learned view-dependent features. Deferred shading is employed, with a small MLP evaluated per ray rather than per sample, and quantized feature representations enable storage in ~90 MB per scene with competitive PSNR/SSIM/LPIPS.

In both approaches, leveraging scene sparsity grants substantial memory and compute savings, at the cost of an up-front “baking” step and increased storage for large or complex environments.

3. Hybrid and Hardware-Optimized Approaches

Further advances address network evaluation and memory bottlenecks through architectural and hardware optimizations. AdaNeRF (Kurz et al., 2022) splits radiance prediction into a sampling network—which learns, per ray, which positions are most informative—and a shading network, which is only evaluated at important samples. The result is high-quality rendering with as few as 2–7 samples per ray, achieving real-time performance while using under 4 MB per scene.

RT-NeRF (Li et al., 2022) pioneers end-to-end algorithm-hardware co-design. On the algorithmic front, it replaces uniform point sampling with a geometry-aware, non-uniform walk over pre-existing (nonzero) occupancy grid cubes and employs a coarse-grained, view-dependent processing order to skip invisible points. On the hardware side, adaptive bitmap/coordinate (COO) encoding for sparse embeddings, and a dual-purpose bidirectional adder/search tree, minimize DRAM access and exploit sparsity. This yields speedups of up to 3,200× compared to NeRF baselines with negligible PSNR loss, enabling immersive AR/VR scenarios on portable devices.

Re-ReND (Rojas et al., 2023) distills a NeRF into a geometry proxy mesh and a light field, stored as matrices for each triangle, enabling single-pass rasterization (via fragment shaders) on standard graphics hardware. This method delivers a 2.6× speedup over previous state-of-the-art methods with competitive PSNR even on mobile devices.

MixRT (Li et al., 2023) demonstrates that even a low-quality mesh, combined with a view-dependent learned displacement map and a compressed neural field (hash-encoded, e.g., Instant-NGP style), suffices for real-time rendering at high quality and storage efficiency—over 30 FPS on MacBook M1 Pro and ~0.2 PSNR higher than recent volumetric methods on challenging datasets.

4. Point, Gaussian, and Triangle Splatting Paradigms

Rasterization of explicit primitives—including points, Gaussians, and triangles—enables high-speed, high-fidelity rendering by leveraging GPU hardware.

3D Gaussian Splatting (Kerbl et al., 2023) models scenes as a set of explicit 3D Gaussians, each defined by position, anisotropic covariance, opacity, and SH color coefficients. Projection to 2D via the camera model yields ellipsoidal “splats,” composited with visibility-aware, front-to-back alpha blending. Adaptive densification (cloning/splitting of Gaussians) and parameter optimization via differentiable rendering achieve state-of-the-art quality (SSIM, PSNR, LPIPS) and real-time frame rates (≥30 FPS at 1080p), with training convergence in 5–40 minutes.

Variants further improve performance and flexibility:

• Isotropic Gaussian Splatting (Gong et al., 21 Mar 2024) reduces each Gaussian to four parameters (position, scalar variance), greatly simplifying merging and splitting, and delivering claimed 100× speedups in training time without significant loss in fidelity.
• RadSplat (Niemeyer et al., 20 Mar 2024) uses a NeRF as a robust prior to initialize and supervise the Gaussian set, applies rigorous contribution-based pruning and per-viewpoint filtering, and attains 900+ FPS on complex scenes without compromising image quality.
• TRIPS (Franke et al., 11 Jan 2024) leverages a trilinear splatting scheme, mapping 3D points to screen-space image pyramids, with a small neural network fusing the multi-resolution renderings. The pipeline is entirely differentiable and supports real-time optimization of point positions and sizes.
• Triangle Splatting (Held et al., 25 May 2025) repositions triangle meshes as optimized differentiable primitives, with each triangle’s contribution modulated by a normalized, signed-distance-based window function. The explicit “triangle soup” blends the efficiency of rasterization with end-to-end learnability, achieving 2,400 FPS at 1280×720 using standard mesh renderers and superior LPIPS (perceptual) quality compared to 2D and 3D Gaussian Splatting and Zip-NeRF.

Each of these representations achieves real-time or near real-time rates and demonstrates the flexibility and compatibility of explicit rasterizable primitives for neural scene reconstruction and rendering.

5. Advanced Features: Dynamic Scenes, HDR, and Cinematic Effects

Several methods now target dynamic scenes, high dynamic range (HDR), and depth-of-field effects.

• Fourier PlenOctrees (FPO) (Wang et al., 2022) extend PlenOctree methodology by encoding temporal variations as Fourier coefficients directly stored in octree leaves, allowing the construction and rendering of dynamic radiance fields at real-time rates (~100 FPS), while preserving high fidelity (PSNR ~35 dB, SSIM ~0.991).
• VideoRF (Wang et al., 2023) serializes dynamic, 4D radiance field data into a sequence of 2D feature images, applied in a deferred shading pipeline. Spatial and temporal consistency is enforced via regularized training losses, allowing extremely compact representations (670 KB per scene) and real-time performance across desktops and mobile devices.
• Cinematic Gaussians (Wang et al., 11 Jun 2024) integrates HDR radiance field reconstruction and depth-of-field synthesis by extending 3D splatting to support analytical convolutions with thin-lens camera models and explicit tone mapping. Exposure, aperture, and focus can be modulated post-capture, all at interactive rates (≈110 FPS).

These advancements address practical requirements for photorealism in consumer and professional visualization contexts, significantly broadening the scope and appeal of neural scene representations.

6. Rasterization and Explicit Grid Techniques

Replacing ray marching with rasterization of explicit 3D grids is a complementary acceleration avenue.

• Sparse Voxel Rasterization (SVRaster) (Sun et al., 5 Dec 2024) employs adaptively allocated octree voxels, each storing SH-based color, and uses a custom rasterizer that sorts voxels for blending via a ray direction–dependent Morton order. The approach avoids the popping artifacts of Gaussian Splatting and delivers more than 10× speedup over prior voxel methods and >4 dB PSNR improvement, with rendering rates above 92–137 FPS.
• Such explicit representations are compatible with well-established 3D processing algorithms (e.g., Marching Cubes, TSDF-fusion), further enhancing their integration potential in reconstruction and visualization pipelines.

7. Multi-View and Light Field Display Rendering

Emerging applications in light field displays necessitate the rendering of many slightly shifted high-resolution views simultaneously.

• A unified plane sweeping and swizzle blending framework (Kim et al., 25 Aug 2025) generalizes across NeRF, Gaussian splatting, and sparse voxel fields for multi-view rendering. The key idea is single-pass slicing of the scene into forward-swept planes, caching non-directional components, and reconstructing the full light field quilt per view through efficient alpha blending and coordinate transformations.

This architecture enables rendering >200 FPS at 512p for 45 light field views (Looking Glass display), representing a 22× speedup over naively rendering each view independently, with no retraining and negligible quality loss.

Conclusion

Real-time radiance field rendering now encompasses an array of synergistic techniques—explicit grid/octree tabulation, deferred or compressed neural architectures, point/Gaussian/triangle-based splatting, hybrid mesh-displacement-neural pipelines, efficient hardware co-design, and multi-view caching. Across these paradigms, the field has achieved several orders-of-magnitude speedups over original NeRF (with benchmarks reaching thousands of FPS on modern hardware), without substantial reductions in visual fidelity on synthetic and real benchmarks. The explicit support for dynamic scenes, HDR imaging, cinematic focus/defocus, editability, and compatibility with established rasterization and graphics stacks further cements the practical significance of these advances for AR/VR, interactive graphics, and real-time cinematic production.