# Neural Voxel Renderer: Learning an Accurate and Controllable Rendering Tool

arXiv: https://arxiv.org/abs/1912.04591 · HTML: https://mlsanity.com/paper/1912.04591/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2019.

## Built on by (most cited first, 20 of 20)

- [NeuMesh: Learning Disentangled Neural Mesh-based Implicit Field for Geometry and Texture Editing](https://mlsanity.com/paper/2207.11911/) (2022)
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- [DIVeR: Real-time and Accurate Neural Radiance Fields with Deterministic Integration for Volume Rendering](https://mlsanity.com/paper/2111.10427/) (2021)
- [RenderFormer: Transformer-based Neural Rendering of Triangle Meshes with Global Illumination](https://mlsanity.com/paper/2505.21925/) (2025)
- [Physical Adversarial Attacks For Camera-based Smart Systems: Current Trends, Categorization, Applications, Research Challenges, and Future Outlook](https://mlsanity.com/paper/2308.06173/) (2023)
- [Known Operator Learning and Hybrid Machine Learning in Medical Imaging -- A Review of the Past, the Present, and the Future](https://mlsanity.com/paper/2108.04543/) (2021)
- [im2nerf: Image to Neural Radiance Field in the Wild](https://mlsanity.com/paper/2209.04061/) (2022)
- [Hyb-NeRF: A Multiresolution Hybrid Encoding for Neural Radiance Fields](https://mlsanity.com/paper/2311.12490/) (2023)
- [IG-SLAM: Instant Gaussian SLAM](https://mlsanity.com/paper/2408.01126/) (2024)
- [Deep Direct Volume Rendering: Learning Visual Feature Mappings From Exemplary Images](https://mlsanity.com/paper/2106.05429/) (2021)
- [Neural Rendering and Its Hardware Acceleration: A Review](https://mlsanity.com/paper/2402.00028/) (2024)
- [Messing Up 3D Virtual Environments: Transferable Adversarial 3D Objects](https://mlsanity.com/paper/2109.08465/) (2021)
- [Understanding Pixel-level 2D Image Semantics with 3D Keypoint Knowledge Engine](https://mlsanity.com/paper/2111.10817/) (2021)
- [ZeroForge: Feedforward Text-to-Shape Without 3D Supervision](https://mlsanity.com/paper/2306.08183/) (2023)
- [Anisotropic Neural Representation Learning for High-Quality Neural Rendering](https://mlsanity.com/paper/2311.18311/) (2023)
- [Structure-Centric Robust Monocular Depth Estimation via Knowledge Distillation](https://mlsanity.com/paper/2410.06982/) (2024)
- [Surface Geometry Processing: An Efficient Normal-based Detail Representation](https://mlsanity.com/paper/2307.07945/) (2023)
- [Abstract Rendering: Computing All that is Seen in Gaussian Splat Scenes](https://mlsanity.com/paper/2503.00308/) (2025)
- [NeuMesh++: Towards Versatile and Efficient Volumetric Editing with Disentangled Neural Mesh-based Implicit Field](https://mlsanity.com/paper/2606.19316/) (2026)

## Abstract (arXiv)

We present a neural rendering framework that maps a voxelized scene into a high quality image. Highly-textured objects and scene element interactions are realistically rendered by our method, despite having a rough representation as an input. Moreover, our approach allows controllable rendering: geometric and appearance modifications in the input are accurately propagated to the output. The user can move, rotate and scale an object, change its appearance and texture or modify the position of the light and all these edits are represented in the final rendering. We demonstrate the effectiveness of our approach by rendering scenes with varying appearance, from single color per object to complex, high-frequency textures. We show that our rerendering network can generate very detailed images that represent precisely the appearance of the input scene. Our experiments illustrate that our approach achieves more accurate image synthesis results compared to alternatives and can also handle low voxel grid resolutions. Finally, we show how our neural rendering framework can capture and faithfully render objects from real images and from a diverse set of classes.