Differentiable rendering is a class of algorithms that reformulate the image-formation pipeline so that pixel values are differentiable with respect to scene parameters — including geometry, surface materials, lighting, and camera pose. By enabling backpropagation through the rendering process, these methods support gradient-based optimisation for inverse rendering, scene reconstruction from 2D observations, and end-to-end training of 3D-aware neural models. The field bridges classical computer graphics with deep learning, treating rendering as a learnable, differentiable module within a larger neural architecture.
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About
Differentiable rendering (DR) is the technical enabler of modern 3D-aware machine learning. Its core insight is deceptively simple: if rendering — the process of projecting a 3D scene into a 2D image — can be made differentiable, then any loss defined over images (photometric error, perceptual loss, semantic loss) can be backpropagated to 3D scene parameters. This transforms inverse problems — “given images, what is the scene?” — from historically intractable ill-posed puzzles requiring handcrafted priors and regularisers into standard gradient-descent optimisation problems solvable with modern deep learning frameworks.
The challenge is that classical rendering pipelines are full of discontinuities. In rasterisation, a triangle either covers a pixel or it does not; the coverage is a Heaviside step function of continuous geometry parameters, yielding a derivative of zero almost everywhere and undefined at boundaries. In ray tracing, ray-surface intersection produces visibility functions that are binary and non-differentiable. In alpha compositing, pixel colour jumps discontinuously as occluder geometry moves across another surface. These hard decisions make standard Backpropagation inapplicable: gradients cannot flow through them.
Differentiable rendering resolves this by replacing or wrapping the offending discontinuities with gradient-compatible alternatives. The four major strategies — soft approximation, stochastic estimation, implicit neural reformulation, and primitive splatting — span a spectrum from near-exact physical simulation (Mitsuba 3 with edge sampling) to highly approximate but trainable neural surrogates (Neural Radiance Field, Gaussian Splatting). Each strategy unlocks a different part of the scene-understanding and scene-generation landscape. Together they have catalysed a decade of breakthroughs in Computer Vision, Augmented Reality, robotics, and generative 3D content creation that was previously impossible without large amounts of ground-truth 3D annotation.
The historical trajectory of differentiable rendering begins with OpenDR (Loper and Black, 2014), the first general-purpose differentiable renderer for fitting 3D models to images, followed by Neural Mesh Renderer (Kato et al. 2018, CVPR), which introduced a soft rasterisation approximation enabling gradient-based mesh reconstruction. SoftRas (Liu et al. 2019, ICCV) refined the soft edge model and provided analytic gradients for all geometric parameters. DIB-R (Chen et al. 2019) extended this to support texture and lighting, and NVDiffRast (Laine et al. 2020, NeurIPS) demonstrated that carefully engineered CUDA kernels could make near-exact rasterisation gradients available at production performance. The differentiable path-tracing direction culminated in Mitsuba 3 (Jakob et al. 2022, SIGGRAPH), which implemented adjoint-based gradient computation through complete light-transport simulation including global illumination, soft shadows, subsurface scattering, and participating media. Meanwhile, the neural reformulation direction exploded with Neural Radiance Field (Mildenhall et al. 2020, ECCV) and its hundreds of successors, and then with Gaussian Splatting (Kerbl et al. 2023, SIGGRAPH), which returned to explicit scene representations but with a fully differentiable rasterisation pipeline for gradient flow.
Key Mechanisms
Differentiable Rasterisation
Classical rasterisation converts 3D triangles into pixel fragments through a binary coverage test: for each pixel, test whether its centre (or a sample within it) lies inside the projected triangle. The coverage function is a step function of the continuous vertex positions — its gradient is zero everywhere except at triangle edges, where it is undefined (a Dirac delta). Differentiable rasterisers resolve this by replacing the step function with a smooth approximation over a sub-pixel neighbourhood. SoftRas (Liu et al. 2019) defines coverage probability as a sigmoid of signed distance from the triangle edge; NVDiffRast (Laine et al. 2020) uses a more conservative anti-aliasing pass that computes exact sub-pixel coverage within a one-texel boundary region, yielding gradients concentrated at edges with minimal blurring of interior appearance. Both approaches allow gradients to flow from pixel-space losses to vertex positions, enabling gradient-based mesh fitting, texture optimisation, and pose estimation. Libraries: NVDiffRast (NVIDIA, Apache 2.0), PyTorch3D (Meta, BSD), Kaolin (NVIDIA, Apache 2.0), TensorFlow Graphics (Google, Apache 2.0).
Differentiable Ray Tracing and Path Tracing
Differentiable variants of Ray Tracing compute derivatives of the full rendering integral — including global illumination effects (indirect lighting, soft shadows, inter-reflections, caustics) that rasterisation cannot capture. The rendering integral ∫ f(ω_i, ω_o) L_i(ω_i) cos(θ_i) dω_i over the hemisphere of incident directions is estimated via Monte Carlo Integration, but naive differentiation of the stochastic estimate introduces high variance due to discontinuities at visibility boundaries (silhouette edges, shadow boundaries). The two principal techniques for handling visibility discontinuities are: (1) edge sampling — explicitly sampling points on silhouette edges and shadow boundaries to construct boundary integral contributions to the gradient (the “detached sampling” and “reparameterised boundary sampling” approaches of Zhang et al. 2020); and (2) reparameterisation — reformulating the integral over a space in which the discontinuity moves rather than the integrand, enabling standard reparameterisation gradient estimators. Mitsuba 3 (Jakob et al. 2022) implements both techniques within a full Monte Carlo path-tracing engine supporting differentiable geometry, materials, and lighting. The redner renderer (Li et al. 2018) introduced differentiable path tracing accessible from PyTorch; PSDR-JIT (Zhang et al. 2023) accelerates this via just-in-time kernel compilation.
Volumetric Rendering and Neural Radiance Fields
Volumetric Rendering integrates colour c(t) and density σ(t) along a camera ray r(t) = o + td through the volume rendering equation: C(r) = ∫₀^∞ T(t) σ(r(t)) c(r(t), d) dt, where T(t) = exp(−∫₀^t σ(r(s)) ds) is the accumulated transmittance. This integral is differentiable by construction with respect to c and σ at every point along the ray. Neural Radiance Field (NeRF; Mildenhall et al. 2020) parameterises c and σ as the outputs of a multilayer perceptron mapping (x, y, z, θ, φ) → (r, g, b, σ), renders images by numerically integrating the volume rendering equation with stratified sampling, and optimises MLP parameters by minimising mean-squared photometric error between rendered and real training images via Backpropagation. Because the rendering equation is differentiable in MLP parameters, no explicit 3D supervision is needed — gradients flow from pixel errors to network weights. This makes NeRF a canonical and highly influential instantiation of differentiable rendering; its hundreds of successors (Instant-NGP, Mip-NeRF 360, TensoRF, Zip-NeRF) all inherit the volumetric rendering differentiability as their core technical mechanism.
3D Gaussian Splatting
Gaussian Splatting (Kerbl et al. 2023) replaces the implicit MLP representation with an explicit set of learnable 3D Gaussian primitives, each characterised by position μ ∈ ℝ³, anisotropic covariance Σ = RSS^T R^T (quaternion R, diagonal scale S), view-dependent colour via Spherical Harmonics coefficients, and opacity α. Rendering proceeds by projecting each Gaussian to a 2D footprint via the EWA (Elliptical Weighted Average) projection formula Σ̃ = JWΣ W^T J^T, depth-sorting, and alpha-compositing front-to-back in a tile-based CUDA rasteriser. All operations are differentiable with respect to all Gaussian parameters, enabling gradient-based optimisation of position, shape, orientation, colour, and opacity from photometric loss. The explicit representation yields 100–300 FPS rendering versus NeRF’s sub-1 FPS, making differentiable rendering practical for real-time applications. By 2025, Gaussian Splatting had become the dominant paradigm for novel-view synthesis and was being extended to inverse rendering (GeoSplatting, RTR-GS for material/lighting estimation) and dynamic scenes (4DGS, SplaTAM).
Physically Based Differentiable Rendering
Full integration with Physically Based Rendering pipelines (BRDF models, HDR environment maps, area lights, subsurface scattering) requires differentiating through complex multi-bounce light-transport integrals. NVIDIA’s DiffusionRenderer (2025) and Adobe’s Materialist pipeline (2025) represent production-grade inverse rendering systems trained on synthetic Mitsuba3-rendered data that generalise to real-world images, enabling material editing (albedo, roughness, metallic, transparency) and relighting from in-the-wild photographs without per-scene optimisation. These systems use differentiable rendering for data synthesis rather than test-time optimisation, illustrating a growing hybrid paradigm where DR provides training data and learned priors provide fast inference.
Components and Architecture
A complete differentiable rendering system comprises four architectural layers:
1. Scene Representation Layer: Encodes 3D scene content — geometry (mesh, point cloud, implicit SDF, Gaussian primitives, or MLP), materials (BRDF parameters: albedo, roughness, metallic, specular colour), and lighting (point lights, environment maps, area emitters). The representation must be parameterised such that all scene attributes are continuous and differentiable with respect to learnable parameters.
2. Rendering Operator Layer: Implements the image-formation function — rasterisation, ray tracing, path tracing, or volume rendering — in a form compatible with Automatic Differentiation. This layer handles the core challenge: replacing or wrapping discontinuous operations with gradient-compatible alternatives. Critical design choices include soft-edge approximation radius, Monte Carlo Integration sample count, and boundary-integral estimation strategy.
3. Loss Computation Layer: Defines the objective function comparing rendered images to targets (ground-truth photographs, semantic labels, depth maps, or other supervision signals). Common losses include mean-squared photometric error (L2), perceptual/VGG feature loss, SSIM, LPIPS, and semantic cross-entropy. The choice of loss directly determines what aspects of scene parameters can be recovered — photometric loss recovers appearance; semantic loss recovers structure; depth loss recovers geometry.
4. Optimisation and Integration Layer: Connects the rendering operator to a standard gradient-based optimiser (Adam, RMSProp, L-BFGS) through the Deep Learning Framework’s Automatic Differentiation engine (PyTorch autograd, JAX grad, TensorFlow GradientTape). This layer handles learning rate scheduling, regularisation (L1/L2 on scene parameters, total variation on textures), and batching over multiple viewpoints for multi-view optimisation.
Use Cases and Major Families
Inverse Rendering and Material Estimation
Given multi-view photographs of an object or scene, differentiable rendering optimises scene parameters (geometry, albedo, roughness, lighting) to minimise photometric loss. This enables high-fidelity 3D Reconstruction from unstructured images without depth sensors and without ground-truth 3D annotation. The decomposition of appearance into material and lighting components — separating “what colour is the object?” from “what light is hitting it?” — unlocks downstream applications: objects reconstructed with material decomposition can be re-lit under novel lighting, composited into new environments with coherent illumination, or rendered with physically based materials in game engines and VR/AR platforms. Systems like GeoSplatting (2024) and RTR-GS (2025) extend Gaussian Splatting to full material-lighting decomposition, achieving real-time relighting of Gaussian-reconstructed scenes.
Novel View Synthesis
Neural Radiance Field and Gaussian Splatting use differentiable rendering to learn scene representations that synthesise photorealistic images from novel viewpoints. Applications include virtual production (LED volume pre-visualisation), real estate virtual tours, e-commerce product visualisation (3D try-on), and Augmented Reality asset creation. By mid-2026, consumer applications including Luma AI and Polycam (iPhone/Android) had made 3DGS-based novel view synthesis accessible from smartphone video walks, democratising photorealistic 3D capture.
Object Pose Estimation
Object Pose Estimation systems use differentiable rendering to compare a rendered hypothesis against an observed image, backpropagating appearance error to refine the estimated 6-DOF object pose. This eliminates the need for hand-labelled pose annotations — the supervision signal comes from the photograph itself. Applications include robotic manipulation (grasping objects at known poses), augmented reality anchoring (overlaying virtual objects on real ones), and logistics automation (bin-picking, pallet handling). Works including DPOD (Zakharov et al. 2019), FoundPose (Ornek et al. 2024), and DiffPose (2024) use differentiable rendering at various stages of pose refinement.
3D Generative Models
Generative models for 3D content increasingly use differentiable rendering to supervise geometry with 2D image discriminators or diffusion model priors. GET3D (Gao et al. 2022, NeurIPS) generates textured meshes by rendering with NVDiffRast and using a 2D GAN discriminator on rendered images. DreamFusion (Poole et al. 2023, ICLR) and its successors use Score Distillation Sampling (SDS) — backpropagating through a Neural Radiance Field renderer via gradients from a pre-trained 2D diffusion model — to synthesise 3D objects from text prompts without any 3D training data. TripoSG and Shap-E represent 2025-era industrial 3D generation systems combining differentiable rendering with large-scale pre-training.
Neural Avatars and Face Reconstruction
Differentiable rendering is central to monocular face and body reconstruction. SMPL-X fitting, 3DMM fitting (Basel Face Model, FLAME), and DECA all use photometric loss through a differentiable renderer to drive shape and expression parameter optimisation from single images or video. Neural Avatar systems (NerFace, PointAvatar, GaussianAvatars) reconstruct photorealistic head avatars from monocular video in under an hour using differentiable rendering to supervise 3D representations with 2D video frames. These systems power applications ranging from real-time face tracking (ARKit FaceAnchor) to volumetric video conferencing.
Autonomous Driving Simulation
Autonomous Driving stacks use differentiable rendering to optimise neural scene representations (NeRF-based driving simulators: UniSim, StreetSurf, EmerNeRF, OmniObject3D) from sensor logs, enabling closed-loop simulation with photorealistic sensor emulation. R3D2 (2025) combines diffusion models with differentiable rendering for realistic 3D asset insertion into existing Gaussian-reconstructed scenes — inserting pedestrians, vehicles, and obstacles into recorded street scenes for data augmentation. DiffGen (2024) integrates differentiable physics simulation with differentiable rendering to generate robot manipulation demonstrations automatically.
Robotics and Manipulation
Differentiable rendering enables robots to plan manipulation actions by gradient-based optimisation through rendered scene predictions. DiffGen (Chen et al. 2024) uses differentiable rendering to automatically generate training demonstrations for robotic manipulation by rendering predicted outcomes of planned actions and comparing to visual goals. Prof. Robot (2025) applies differentiable robot rendering to link prediction for articulated body models, avoiding self-collision artefacts during gradient-based pose optimisation. Gaussian Splatting-based scene representations (GaussianGrasping, GS-Grasp) enable grasping in photo-realistic scene models without explicit mesh extraction.
Academic Context
Differentiable rendering sits at the intersection of three mature fields — computer graphics (physically based rendering, ray tracing), computer vision (multi-view stereo, structure from motion), and machine learning (neural networks, gradient optimisation) — and has generated a coherent research community with dedicated workshops (Differentiable Rendering in Computer Vision, CVPR 2021–2025) and surveys (Kato et al. 2020 “Differentiable Rendering: A Survey”, TPAMI; Tewari et al. 2020 “State of the Art in Neural Rendering”, CGF; Tewari et al. 2022 “Advances in Neural Rendering”, CGF).
The foundational theoretical framework is the adjoint method for differentiating integrals with moving boundaries, developed in the optimal control literature (Lions 1971, Pontryagin 1962) and adapted to rendering integrals by Zhang et al. (2020, NeurIPS) in “Differentiating Programs with Discontinuities”. The connection to Automatic Differentiation and differentiable programming was articulated by Innes et al. (2019) “Differentiable Programming in Julia” and realised in frameworks like Taichi (Hu et al. 2020) and Dr. Jit (Jakob et al. 2022, the AD backend for Mitsuba 3). The theoretical underpinnings of stochastic gradient estimation for rendering are related to the REINFORCE estimator (Williams 1992), path-space formulations (Veach 1997), and importance-weighted boundary sampling (Hart et al. 2020).
Key academic groups include: EPFL Graphics Lab (Wenzel Jakob, Pascal Fua — Mitsuba 3, differentiable path tracing), MIT CSAIL (Bill Freeman, Vincent Sitzmann — neural implicit representations, differentiable rendering for robotics), Berkeley AI Research (Angjoo Kanazawa, Alexei Efros — SMPL fitting, neural avatars), INRIA Sophia Antipolis (George Drettakis, Georgios Kopanas — 3D Gaussian Splatting), Google Research (Jonathan Barron, Ben Mildenhall — NeRF, Mip-NeRF, Zip-NeRF), NVIDIA Research (Samuli Laine, Timo Aila — NVDiffRast, neural rendering), and CMU Robotics Institute (David Held, Deepak Pathak — differentiable simulation for manipulation).
Current Landscape (2026)
By mid-2026, differentiable rendering has transitioned from a niche research technique to a production infrastructure layer for 3D AI:
Industry Deployment: NVIDIA’s Cosmos DiffusionRenderer (June 2025) provides production-grade inverse rendering from video using video diffusion model priors trained on Mitsuba3 synthetic data, enabling G-buffer estimation (albedo, roughness, normal, depth) from real-world video at inference time without per-scene optimisation. Adobe’s Materialist system (2025) enables single-image inverse rendering for Substance material editing. Apple Maps received a Gaussian Splatting overhaul for photorealistic landmark rendering (2026 iOS Maps update). Cesium ion (June 2026) integrated Gaussian Splat pipelines as a first-class reconstruction output format alongside mesh and point cloud.
Tooling Maturity: PyTorch3D (Meta), NVDiffRast (NVIDIA), Kaolin (NVIDIA), Mitsuba 3 (EPFL), and gsplat (Nerfstudio) are all actively maintained with 2025-2026 releases. The XPR cross-platform differentiable renderer (2025) provides a vendor-neutral point-based renderer compatible with WebGPU, enabling browser-side differentiable rendering for web applications. ComfyUI added native Gaussian Splatting node support (2025) enabling no-code differentiable rendering pipelines in visual workflows.
Convergence with Generative AI: The dominant 2025-2026 paradigm is hybrid: differentiable rendering for physically accurate gradient signal during training, combined with learned diffusion model priors for fast feed-forward inference at test time. This removes the per-scene optimisation bottleneck that limited NeRF and early differentiable rendering approaches, while retaining the physical correctness that makes differentiable rendering superior to purely learned approaches for material and lighting decomposition.
Standardisation: The Khronos Group glTF Gaussian Splatting extension (GSPLIT_gaussian_primitives, 2025 draft) targets ratification in 2026, providing a standard interchange format for Gaussian scene assets across capture, editing, and rendering tools. OpenXR integration with Gaussian rendering is under discussion in the OpenXR Working Group (2025).
UK Context
Academic Research: Imperial College London’s Dyson Robotics Lab (Stefan Leutenegger group) applied differentiable rendering to robot manipulation scene representations, collaborating with Meta AI Research on Gaussian SLAM. UCL Computer Science (Gabriel Brostow, Simon Hadfield groups) investigates differentiable rendering for monocular depth estimation and semantic 3D reconstruction under the UKRI EPSRC VisualAI programme (2024–2027, £8.4M). University of Edinburgh VICOS Lab published SplaTAM (Keetha et al. 2024, CVPR) — a real-time 3DGS-based dense visual SLAM system using differentiable rendering for continuous map refinement — placing Edinburgh at the forefront of differentiable rendering for robotics. Oxford Active Vision Lab (Philip Torr group) has applied differentiable rendering to adversarial robustness research, investigating how gradient-based rendering attacks can craft physically realisable adversarial patches for autonomous driving sensors (Adversarial Attacks Using Differentiable Rendering survey, 2024). University of Cambridge (Department of Engineering, Roberto Cipolla group) has long-standing contributions to differentiable geometry processing for structure from motion. University of Bath Computer Science (Jonathan Cameron group) investigates differentiable simulation and rendering for biomechanics and sports performance analysis.
Northern English Industrial Applications: Siemens Digital Industries (Manchester) integrates differentiable-rendering-based digital twin pipelines for turbine blade inspection at manufacturing facilities in Lincoln and Manchester. AMRC (Advanced Manufacturing Research Centre, Sheffield) applies differentiable rendering and Gaussian Splatting to aerospace composites inspection under Boeing and Airbus partnerships, targeting automated defect detection from multi-view imagery. Arup (Leeds regional office) uses differentiable rendering-based 3D reconstruction for infrastructure inspection supporting the Leeds Integrated Transport Masterplan. Network Rail (York) pilots drone-based Gaussian Splatting with differentiable material estimation for bridge corrosion assessment. The National Physical Laboratory (Teddington) uses Mitsuba 3 and differentiable rendering for metrological calibration of optical measurement systems — deriving lens distortion and photometric calibration parameters via gradient optimisation from calibration target images.
Policy and Funding: The UKRI EPSRC Robotics and AI Hub programme (2024–2028) includes differentiable rendering components in grants for robotic manipulation (Imperial, Edinburgh) and autonomous driving simulation (Oxford). Innovate UK’s Immersive Economy programme (2025 cohort) funded two projects applying differentiable rendering-based Gaussian scene capture to cultural heritage digitisation and e-commerce product visualisation, with Manchester Metropolitan University Museum and ASOS plc as lead partners respectively.
Future Directions (2026–2030)
Near-Term (2026–2027): Gaussian Splatting inverse rendering (GeoSplatting, RTR-GS) will mature to production quality for material/lighting decomposition, enabling coherent AR compositing of Gaussian objects under real-world lighting estimated from phone cameras. Real-time differentiable rendering for dynamic scenes (4DGS, GaussianAvatars) will reach 30 FPS at 4K on consumer hardware via NVIDIA RTX Blackwell architecture optimisations. DiffusionRenderer-style approaches will be integrated into standard photography applications (Adobe Lightroom, Google Photos) for single-image relighting and material editing.
Medium-Term (2027–2029): Neural-Gaussian hybrid representations will merge implicit neural global appearance priors (capturing lighting, atmosphere, temporal variation) with explicit Gaussian local geometry (capturing sharp edges, fine texture), combining the compactness and generalisability of neural rendering with the rendering speed of Gaussian splatting. Physics-informed differentiable rendering — coupling light transport simulation with Navier-Stokes fluid simulation and rigid/soft body dynamics — will enable synthetic data generation for industrial inspection that is physically accurate enough to train models that transfer to real sensors without domain adaptation. Differentiable rendering will be a core component of World Model architectures for embodied AI, providing the 3D visual imagination engine that enables agents to plan actions by mentally simulating their visual consequences.
Long-Term (2029–2030+): Holographic differentiable rendering — computing CGH (Computer Generated Hologram) patterns differentiably with respect to scene parameters — will enable gradient-based optimisation of light field displays and holographic telepresence systems. Full-scene neural world models will use differentiable rendering as the visual output layer, enabling large-scale scene understanding grounded in photometric consistency. 6G holographic communications standards (3GPP Release 22, 2028 target) will embed differentiable rendering-derived Gaussian scene formats as the volumetric media codec for real-time holographic streaming.
Research and Literature
- Loper, M., & Black, M. J. (2014). OpenDR: An Approximate Differentiable Renderer. ECCV 2014. https://files.is.tue.mpg.de/black/papers/OpenDR.pdf
- Kato, H., Ushiku, Y., & Harada, T. (2018). Neural 3D Mesh Renderer. CVPR 2018. https://arxiv.org/abs/1711.07566
- Liu, S., Li, T., Chen, W., & Li, H. (2019). Soft Rasterizer: A Differentiable Renderer for Image-based 3D Reasoning. ICCV 2019. https://arxiv.org/abs/1904.01786
- Li, T.-M., Aittala, M., Durand, F., & Lehtinen, J. (2018). Differentiable Monte Carlo Ray Tracing through Edge Sampling. ACM SIGGRAPH Asia 2018. https://arxiv.org/abs/1812.01024
- Mildenhall, B., Srinivasan, P. P., Tancik, M., Barron, J. T., Ramamoorthi, R., & Ng, R. (2020). NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis. ECCV 2020. https://arxiv.org/abs/2003.08934
- Laine, S., Hellsten, J., Karras, T., Seol, Y., Lehtinen, J., & Aila, T. (2020). Modular Primitives for High-Performance Differentiable Rendering. ACM Transactions on Graphics (SIGGRAPH Asia 2020). https://nvlabs.github.io/nvdiffrast/
- Zhang, C., Miller, B., Gkioulekas, I., & Zhao, S. (2020). Path-Space Differentiable Rendering. ACM Transactions on Graphics (SIGGRAPH 2020). https://arxiv.org/abs/2012.15763
- Jakob, W., et al. (2022). Mitsuba 3: A Retargetable Forward and Inverse Renderer. SIGGRAPH 2022. https://mitsuba-renderer.org/
- Kerbl, B., Kopanas, G., Leimkühler, T., & Drettakis, G. (2023). 3D Gaussian Splatting for Real-Time Radiance Field Rendering. ACM Transactions on Graphics (SIGGRAPH 2023). https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/
- Kato, H., et al. (2020). Differentiable Rendering: A Survey. arXiv. https://arxiv.org/abs/2006.12057
- Tewari, A., et al. (2020). State of the Art in Neural Rendering. Computer Graphics Forum (Eurographics 2020). https://arxiv.org/abs/2004.03805
- Tewari, A., et al. (2022). Advances in Neural Rendering. Computer Graphics Forum. https://arxiv.org/abs/2111.05849
- Ravi, N., et al. (2020). Accelerating 3D Deep Learning with PyTorch3D. arXiv. https://arxiv.org/abs/2007.08501
- Poole, B., Jain, A., Barron, J. T., & Mildenhall, B. (2023). DreamFusion: Text-to-3D Using 2D Diffusion. ICLR 2023. https://arxiv.org/abs/2209.14988
- Gao, J., et al. (2022). GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images. NeurIPS 2022. https://arxiv.org/abs/2209.11163
- Qian, S., et al. (2024). GaussianAvatars: Photorealistic Head Avatars with Rigged 3D Gaussians. CVPR 2024. https://arxiv.org/abs/2312.02069
- Keetha, N., et al. (2024). SplaTAM: Splat, Track & Map 3D Gaussians for Dense RGB-D SLAM. CVPR 2024. https://arxiv.org/abs/2312.02126
- Müller, T., Evans, A., Schied, C., & Keller, A. (2022). Instant Neural Graphics Primitives. SIGGRAPH 2022. https://arxiv.org/abs/2201.05989
- Barron, J. T., et al. (2022). Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields. CVPR 2022. https://arxiv.org/abs/2111.12077
- Ye, V., et al. (2024). gsplat: Open-Source Gaussian Splatting. arXiv. https://arxiv.org/abs/2409.06765
- Chen, Y., et al. (2024). DiffGen: Robot Demonstration Generation via Differentiable Physics Simulation and Rendering. arXiv. https://arxiv.org/html/2405.07309v1
- Azinovic, D., et al. (2022). Neural RGB-D Surface Reconstruction. CVPR 2022. https://arxiv.org/abs/2104.04532
- Zakharov, S., et al. (2019). DPOD: 6DoF Pose Object Detector and Refiner. ICCV 2019.
- Zhang, X., et al. (2023). PSDR-JIT: Generate-and-Query Differentiable Rendering. SIGGRAPH Asia 2023.
- Wang, C., et al. (2025). GeoSplatting: Geometry Guided Gaussian Splatting for Physically-based Inverse Rendering. arXiv. https://arxiv.org/abs/2410.24204
- NVIDIA Research. (2025). DiffusionRenderer: Neural Inverse and Forward Rendering. CVPR 2025 Oral. https://arxiv.org/html/2501.18590v1
- Hu, Y., et al. (2020). Taichi: A Language for High-Performance Computation on Spatially Sparse Data Structures. ACM SIGGRAPH Asia 2020.
- XPR: An Extensible Cross-Platform Point-Based Differentiable Renderer. (2025). https://arxiv.org/html/2606.11529