Neural Rendering Techniques

Updated 17 November 2025

Neural rendering techniques are computational methods that synthesize images from learned scene representations by integrating deep neural networks with classic graphics principles.
They employ diverse pipelines—such as volumetric rendering (NeRF), implicit surface modeling, point splatting, and mesh-based methods—to accurately simulate lighting, appearance, and view-dependent effects.
Recent advances focus on real-time performance, efficient mobile deployment, and disentangling geometry from appearance, supporting dynamic scenes and interactive AR/VR applications.

Neural rendering encompasses a collection of computational approaches that synthesize photorealistic (or stylized) images from learned scene representations, combining classic graphics principles with deep neural networks. These frameworks can precisely simulate shape, appearance, lighting, and view-dependent effects by training on multi-view data, enabling applications that include novel viewpoint synthesis, real-time AR/VR, global illumination, relighting, and dynamic scene capture. Techniques are grounded in differentiable rendering algorithms, including volume integration, surface tracing, point/voxel splatting, and mesh rasterization, with neural networks inferring colors, densities, BRDFs, and reflectance properties. Recent advances have focused on bridging fidelity and efficiency, disentangling geometry from appearance, and supporting deployment on mobile or embedded hardware.

1. Core Scene Representations and Rendering Pipelines

Neural rendering pipelines are characterized by their scene parameterization and image formation method:

Volumetric approaches (NeRF and derivatives) encode a scene as a continuous radiance field $F_{\sigma,c}(x,d): \mathbb{R}^3 \times S^2 \to (\sigma, c)$ , with neural networks predicting density $\sigma(x)$ and view-dependent color $c(x,d)$ ; images are produced by integrating along rays via discrete quadrature:

$\hat C(r) = \sum_{i=1}^{N} T_i (1 - e^{-\sigma_i \delta_i}) c_i,\quad T_i = \exp\Big(-\sum_{j<i} \sigma_j \delta_j\Big)$

Implicit surfaces (NeuS, KiloNeuS) model geometry as signed distance functions $f(x)$ , producing surfaces as $f(x)=0$ ; rendering uses sphere-tracing and view-conditioned MLP appearance models (with analytic normals for accurate shading and path tracing) (Esposito et al., 2022).
Point-based techniques (3D Gaussian Splatting, PBNR) represent a scene by a collection of colored, anisotropic points or ellipsoids, blended in image space using differentiable kernels (Gaussian/EWA); colors can be parameterized by local SH coefficients or tiny MLPs, with compositing via

$C(p) = \sum_{i=1}^N \left[\Gamma_i \alpha_i c_i\right],\quad \Gamma_i = \prod_{j=1}^{i-1} (1 - \alpha_j)$

Mesh-based and neural texture pipelines (NLR, NeuTex) export explicit geometry (via marching cubes on learned SDFs) and blend multiple view-dependent textures via unstructured lumigraph weights, enabling real-time rasterization and hardware acceleration (Kellnhofer et al., 2021, Xiang et al., 2021).
Hybrid models and neural scene baking integrate classic G-buffer generation (normals, albedo, positions, roughness) with permutation-invariant neural blending and bake global illumination into compact networks for interactive deployment, notably supporting transparency and variable scene structure (Zhang et al., 29 May 2024).

2. Efficiency and Scalability: Real-Time and Mobile Solutions

With neural rendering’s growing adoption in AR/VR and mobile deployments, substantial focus has shifted to achieving real-time performance and scalability:

Hardware-algorithm codesign: Lumina exemplifies co-designed acceleration by integrating two system-level optimizations—sorting-shared (S²) rendering and radiance caching (RC)—with a bespoke accelerator, LuminCore, realizing a measured $4.5\times$ speedup and $5.3\times$ energy reduction on mobile SoCs compared to a Volta GPU baseline, with only $<0.2$ dB loss in PSNR (Feng et al., 6 Jun 2025). The S² path leverages temporal coherence, sharing sorting of Gaussians between adjacent frames (with speculative pre-sorting and viewport padding); RC skips redundant integration for pixels traversing the same major Gaussians (empirical hit rate $H\simeq 80\%$ , theoretical reduction ratio $\eta\simeq 0.55$ ).
Sparse neural inference: KiloNeuS partitions the scene into thousands of micro-MLPs for local SDF/color, allowing GPU-accelerated ray-casting and interactive path tracing at $>40$ FPS and high image fidelity (Esposito et al., 2022). Sphere-tracing and in-shader inference circumvent the bandwidth limitations of traditional MLPs.
Point-based pruning and foveation: MetaSapiens introduces efficiency-aware pruning (ranking Gaussian points by their value-to-computation ratio) and foveated rendering to relax quality in peripheral regions. The system achieves up to $7.4\times$ speedup versus previous state-of-the-art, with PSNR within $0.3$ dB of unpruned baselines, and real-time rates (102–150 FPS at 1080p) on Jetson AGX Xavier/Volta (Lin et al., 29 Jun 2024).
Cache-based acceleration: Radiance caching mechanisms (as in Lumina) exploit the partial path invariance among pixels and time, using 4-way set-associative caches with tags derived from Gaussian IDs; hits allow bypassing nearly all downstream color integration, substantially shrinking runtime.
Foveated volume rendering: FoVolNet employs spatio-temporal blue-noise sampling and stream compaction with W-Net-based inference (direct and kernel prediction stages), producing perceptually high-quality frames at $\sim$ 3.3–4 $\times$ speedup over standard DVR at 1280×720, with average PSNR $\sim$ 33.6 dB (Bauer et al., 2022).

3. Disentangled Geometry, Appearance, and Editing

Recent neural rendering frameworks place strong emphasis on the separation of scene geometry and appearance for interpretability and editability:

Explicit texture mapping: NeuTex disentangles geometry (volumetric density) from appearance (continuous 2D neural texture), learning a cycle-consistent 3D→2D ( $\phi$ ) and 2D→3D ( $\psi$ ) mapping between volume and surface parameterization. The neural texture can be extracted, manually edited (e.g. in Photoshop), and painted back for re-rendering, giving direct control over appearance (Xiang et al., 2021).
Multi-map output for composition: Rig-space Neural Rendering predicts albedo, normal, depth, and mask feature maps directly from rig parameters, enabling downstream dynamic relighting and depth-based composition with other 3D objects (billboarding and z-buffer tests); training uses per-pixel regression with progressive upsampling and multi-branch MLP heads (Borer et al., 2020).
Low-rank neural BRDFs: NeuFace incorporates a low-rank prior for spatially-varying BRDFs, expressing reflectance as a linear combination of global, learned basis functions $b_j(\omega_i,\omega_o)$ weighted by local coefficients $c_j(x)$ , mitigating ambiguity and improving physical plausibility of facial renderings (Zheng et al., 2023).

4. Advanced Illumination and Transparency

Accurate simulation of view-dependent effects, global illumination, and transparency has required novel neural extensions of physically based rendering equations:

Global illumination via radiance caches and semi-gradients: The semi-gradient approach to neural radiosity (Cho et al., 14 Oct 2024) achieves unbiased, low-variance training of radiance field caches by ignoring derivatives through the right-hand side of the rendering equation, converging provably to the true light equilibrium. Training time improves by $\sim$ 25–30%, with error reduction %%%%23 $<0.2$ 24%%%% over full-gradient baselines.
Permutation-invariant transparency rendering: Neural Scene Baking (Zhang et al., 29 May 2024) introduces neural blending functions $T$ that sum per-layer latent vectors, producing results invariant to transparency layer order—a break from conventional alpha-compositing. Separate G-buffers for opaque and transparent objects preserve information behind glass, enabling image synthesis of variable scenes at real-time rates (up to 63 FPS at 256²).
Participating media and global illumination: Participating media neural rendering (Zheng et al., 2021) resolves issues of energy loss and relighting by decomposing in-scattering into direct (ray-traced) and indirect (spherical harmonics) terms, allowing learned disentanglement of density, scattering albedo, and phase function and explicit control over scene lighting and material parameters.

5. Support for Dynamic Scenes and Tomography

Neural rendering models have been extended for time-varying and deformable scenes, including human performances and dynamic tomography:

Spline-based dynamic tomography: Neural radiance fields can reconstruct temporally evolving 3D scenes by modeling a canonical density plus spline-based displacement fields parameterized as B-spline lattices with weights predicted by MLPs (Grega et al., 27 Oct 2024). Training optimizes projection and (optionally) volumetric correlation losses; analysis of pairwise mutual information supports optimal view-angle selection for sparse tomography.
Hybrid neural tracking and animated meshes: Human performance modeling (Zhao et al., 2022) combines explicit embedded deformation tracking with a dynamic 4D hash-encoded neural deformation net, producing high-quality, bandwidth-reduced, animated mesh sequences suitable for real-time AR/VR insertion and occlusion-aware blending.
Generalizable patch-based novel view synthesis: Patch-based neural rendering (Suhail et al., 2022) leverages epipolar geometry for local patch extraction, canonicalizes all positional signals, and uses transformers for cross-view aggregation—significantly improving generalization and inference efficiency for novel scenes, with data efficiency ( $\sim$ 11% prior state-of-the-art) and competitive PSNR.

6. Experimental Metrics, Trade-offs, and Ablation Findings

Neural rendering research systematically evaluates quality, speed, and resource requirements:

Method / Paper	PSNR (dB)	SSIM	FPS	Key Hardware	Memory	Notable Trade-offs
Lumina (Feng et al., 6 Jun 2025)	$-$ 0.20	$-$	218/98	Mobile Volta GPU	$-$	$N$ ↑ $\to$ Speedup↑,PSNR$\down$; RC $k$ ↑ $\to$ PSNR↑,but hit rate$\down$
KiloNeuS (Esposito et al., 2022)	30.77	0.833	46	CUDA GPU	0.2 GB	Local coherence loss—piecewise MLPs; competitive Chamfer
MetaSapiens (Lin et al., 29 Jun 2024)	$-$ 0.3	$-$	150/102	Jetson Xavier/Volta	10–16% base	Hierarchical pruning; foveation blending
FoVolNet (Bauer et al., 2022)	33.6	0.96	$-$	Titan RTX	56 MB	Direct+kernel fusion essential; quantization trade-offs
Neural Voxel Renderer	14.3	0.012	$-$	$-$	$-$	Graceful degradation at low grid res

Ablation studies reveal the value of canonicalization, transformer stages, sorting/caching windows, kernel smoothness (in adaptive shells (Wang et al., 2023)), multi-branch outputs, and low-rank priors, with typical trade-offs—higher quality vs lower speed or increased storage.

7. Emerging Challenges and Future Directions

Neural rendering continues to evolve:

Scalability and universality: Development is moving towards models that generalize across scenes and objects, leveraging foundation models and large-scale priors (Tewari et al., 2021, Tewari et al., 2020).
Efficient differentiable editing: Lightweight neural operators for semantic or appearance edits, as well as reliable geometry-appearance-illuminance disentanglement, remain active research challenges.
Hardware-tailored models: Co-design approaches for mobile/embedded deployment (as in Lumina, MetaSapiens) currently set benchmarks for energy and throughput; investigation continues into cache utilization, quantization robustness, and multi-modal integration.
Transparency, multi-object, and dynamic support: Order-invariant neural compositors and canonicalization architectures are showing promise for robust, scalable integration of variable transparent objects and temporally varying geometry.
Ethics, forensics, and sustainability: The sophistication of neural rendering exacerbates risks including deepfakes and synthetic media abuse, necessitating research into watermarking, provenance, and responsible model release.

Neural rendering has redefined the graphics pipeline, opening avenues for both photorealistic and semantically controllable image synthesis, with real-time capability now routinely achieved on consumer-grade and mobile hardware. Continued innovation will address large-scale scene complexity, direct manipulation of learned latent fields, and robust deployment in diverse physical and social environments.