Neural Rendering Fundamentals

Updated 10 January 2026

Neural rendering is a technique that uses deep neural networks to learn implicit and hybrid scene representations for synthesizing photorealistic images.
It combines data-driven methods with physically motivated image formation to enable effects like anisotropy, global illumination, and transparency.
Practical implementations achieve real-time performance and high image quality through advanced architectures, hardware acceleration, and specialized training techniques.

Neural rendering is an approach in computer graphics and vision that synthesizes photorealistic images by coupling scene representations parameterized by deep neural networks with physically motivated or task-driven image formation algorithms. Unlike traditional rendering, which relies on hand-designed geometry, materials, and lighting, or classical inverse rendering, which seeks explicit reconstruction of these parameters, neural rendering leverages data-driven methodologies to learn implicit or hybrid scene representations from images and render new views, novel relightings, or even dynamic effects. The resulting models can provide high realism, 3D-consistency, explicit user control, and broad applicability, including free-viewpoint navigation, real-time display, scene editing, and cross-domain synthesis.

1. Core Principles and Mathematical Formulation

The foundational paradigm in neural rendering is the use of coordinate-based networks—typically multilayer perceptrons (MLPs)—to model scene attributes such as density, color, and reflectance. Given a 3D point (and often direction and/or time), the network outputs geometry or appearance properties. The canonical neural volume rendering equation, as in Neural Radiance Fields (NeRF), is:

$C(\mathbf{r}) = \int_{t_n}^{t_f} T(t)\,\sigma(\mathbf{r}(t))\,\mathbf{c}(\mathbf{r}(t),\mathbf{d})\,dt$

with transmittance

$T(t) = \exp\left(- \int_{t_n}^t \sigma(\mathbf{r}(s)) ds\right).$

This continuous integral is typically discretized as

$\hat{C}(\mathbf{r}) = \sum_{i=1}^N T_i (1 - e^{- \sigma_i \Delta t_i }) c_i,$

for stratified samples along ray $\mathbf{r}$ , where $T_i = \exp(- \sum_{j<i} \sigma_j \Delta t_j )$ (Tewari et al., 2021, Yan et al., 2024, Wang et al., 2023).

Alternative formulations include surface-based rendering with implicit signed distance functions (SDFs), direct light-field parameterizations $L(r): \mathbb{R}^4 \to \mathbb{R}^3$ , and hybrid or rasterization-pipeline networks with neural textures (Tewari et al., 2021, Kellnhofer et al., 2021, Suhail et al., 2021).

Training is supervised by photometric (pixelwise) reconstruction losses, optionally augmented by structural/SSIM, perceptual, or regularization terms such as the Eikonal constraint for SDFs.

2. Scene Representations and Neural Architectures

Neural rendering encompasses a spectrum of scene representations:

Volumetric MLPs (NeRF variants): Learn a mapping $(x,y,z,\mathbf{d}) \mapsto (\sigma, c)$ with positional encodings, optimized scene-wise or amortized (Tewari et al., 2021, Wang et al., 2023, Yan et al., 2024).
Implicit Surfaces (SDFs): Implicit level-set representations $f_\theta(\mathbf{x})=0$ for surfaces, typically used in combination with neural (view-dependent) texture decoders (Kellnhofer et al., 2021).
Explicit voxel grids / neural voxels: Discrete 3D feature or color grids processed via 3D and 2D CNNs, suitable for efficient scene manipulation and rapid inference (Rematas et al., 2019).
Mesh-based neural rendering: Triangular geometry with neural textures or neural graph convolution for relighting; see deferred neural rendering and mesh-conditioned transformer models (Zeng et al., 28 May 2025, Chen et al., 2019).
Light field and patch-based models: Directly encode high-dimensional light field or patch correspondence information, utilizing transformers and attention to exploit multiview consistency and generalization (Suhail et al., 2021, Suhail et al., 2022).
Hybrid pipelines: Combine classic rasterization (G-buffers, mesh rendering) or physical simulation (scattering-based bokeh) with neural refinement or order-independent blending (Peng et al., 2022, Zhang et al., 2024).

Advanced networks incorporate harmonics (spherical or positional), rotary or frequency-based encodings, hierarchical hash grids, and transformer-based sequence models for both geometric and spatial generalization.

3. Advances: Anisotropy, Global Illumination, and Transparency

Recent innovations have addressed canonical limitations of early neural renderers:

Anisotropic Features: Standard isotropic intervals in NeRF ignore viewing-direction dependence within ray segments, causing ambiguous geometry and blurry results. Anisotropic neural representation learning injects spherical harmonic-guided coefficients for both opacity and latent features into the first MLP, with direction-dependent reconstruction and explicit regularization of anisotropic energy (Wang et al., 2023). This decouples geometry from direction, restoring correct angular opacity and yielding sharper geometry and highlights.
Global Illumination: Transformer-based approaches such as RenderFormer model all-to-all triangle-wise light transport and view-dependent effects via two stages (triangle-to-triangle and triangle-to-ray), obviating per-scene retraining or recursion (Zeng et al., 28 May 2025). The mesh-centric pipeline produces physically plausible global illumination, including multiple-bounce and interreflection effects, in a single forward pass.
Transparency and Permutation Invariance: Complex glass or transparent objects occlude conventional G-buffers, corrupting downstream neural synthesis. Novel real-time neural baking models keep per-object G-buffers and blend them with a permutation-invariant neural aggregator—analogous to PointNet's symmetric functions—allowing accurate, order-independent rendering of scenes with arbitrary transparent layers and full global illumination (Zhang et al., 2024).

4. Practical Systems and Applications

Neural rendering underpins a variety of practical systems:

Remote Real-time Rendering: Frameworks such as NeARportation split latency-constrained rendering between client and server—with instant NeRF-based neural renderers generating HD stereo views on the server, and clients streaming texture-mapped results, supporting 35–40 fps full-HD stereo under photogrammetric capture (Hiroi et al., 2022).
Fast and Mobile Rendering: Platforms like Lumina leverage both algorithmic and hardware-level optimization, exploiting inter-frame coherence and radiance caching in 3D Gaussian Splatting. Co-design with custom SoC accelerators (e.g., LuminCore) yield >4× speed and >5× energy savings with negligible quality loss, achieving real-time performance on edge devices (Feng et al., 6 Jun 2025).
Amortized and Generalizable Pipelines: Equivariant networks enforce SE(3) equivariance, allowing single-pass novel-view synthesis from a single image with no 3D or pose supervision (Dupont et al., 2020). Patch-based transformer approaches construct 3D-consistent outputs directly from small scene patches, encoding rays in canonicalized coordinates for per-patch self- and cross-attention, allowing superior generalization to previously unseen scenes (Suhail et al., 2022).
Editing, Relighting, and Scene Reasoning: Neural rendering has been extended to controllable relightable neural meshes (Chen et al., 2019), rig-driven character animation (Borer et al., 2020), hybrid classical–neural bokeh simulation (Peng et al., 2022), and amodal 3D scene understanding with relational optimization (Yang et al., 2022).

5. Training, Hardware Acceleration, and Performance

Neural rendering is computationally intensive due to the need for hundreds of MLP queries per ray and, for volumetric models, complex memory and dataflow patterns. This has spurred:

Accelerated Training: Multi-resolution hash encoding, e.g., instant-ngp, achieves near-instant NeRF optimization (∼5 seconds per scene/training) (Yan et al., 2024).
Unified Hardware: Chips such as Uni-Render implement a reconfigurable PE array with support for all major neural rendering micro-operators, dynamically switching dataflow and reduction modes to execute mesh-based, volumetric, hash-grid, and Gaussian-splat pipelines in real time under tight energy and area budgets (Li et al., 31 Mar 2025).
Custom ASIC Designs: Neural-field processors, on-chip RMCM-based MLP inference, and fine-grained dataflow adaptation yield up to 119× speedup and 350× energy efficiency versus commodity mobile GPUs (Yan et al., 2024, Li et al., 31 Mar 2025).

Empirically, state-of-the-art models achieve PSNR of ~34 dB and SSIM of 0.96+ on synthetic NeRF benchmarks with anisotropic representations (Wang et al., 2023), and >30 dB with real-time stereo neural rendering pipelines (Hiroi et al., 2022). For transparency, order-independent neural baking delivers up to 36.8 dB/SSIM 0.95+ with real-time performance (Zhang et al., 2024).

6. Limitations and Future Directions

While neural rendering has advanced rapidly, several open challenges remain:

Generalization: Many models still require per-scene optimization; scene-general or amortized models can blur uncertain regions or demand prohibitive compute.
Scalability: Handling tens of thousands of triangles, city-scale scenes, or long video sequences remains limited by attention/memory scaling in transformers or sampling density in volumetric methods (Zeng et al., 28 May 2025, Tewari et al., 2021).
Dynamic/Non-Rigid Scenes: Extending anisotropic and global illumination networks to dynamic, deformable, or unconstrained scenes is a primary research frontier (Wang et al., 2023).
Controllability and Editability: Direct manipulation of neural scene weights and features for artistic purposes is not yet as intuitive or robust as for classical models (Tewari et al., 2021, Tewari et al., 2020).
Integration with Graphics Pipelines: Realizing seamless hybrid pipelines where neural modules augment, not replace, classical rendering remains an ongoing system and API challenge.

Anticipated directions include: learned dynamic anisotropic expansions, adaptive attention for scalable transformers, radiance field priors for dynamic tomography (Grega et al., 2024), and standardized frameworks bridging neural, classical, and hardware-accelerated rendering systems (Li et al., 31 Mar 2025, Yan et al., 2024).

Neural rendering is now the core of high-fidelity, controllable, and efficient image synthesis across static, dynamic, and hybrid visual computing tasks. It continues to unify statistical scene learning with physical rendering, scaling from photorealistic relighting to mobile AR/VR and X-ray tomography, while driving advances in algorithm–hardware co-design (Tewari et al., 2021, Zeng et al., 28 May 2025, Wang et al., 2023, Zhang et al., 2024).