# SiLVR: Scalable Lidar-Visual Reconstruction with Neural Radiance Fields for Robotic Inspection

arXiv: https://arxiv.org/abs/2403.06877 · HTML: https://mlsanity.com/paper/2403.06877/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 43 references that are in the map.
- First posted on arXiv 11 Mar 2024.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields](https://mlsanity.com/paper/2111.12077/) (2021)
- [NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction](https://mlsanity.com/paper/2106.10689/) (2021)
- [Plenoxels: Radiance Fields without Neural Networks](https://mlsanity.com/paper/2112.05131/) (2021)
- [NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections](https://mlsanity.com/paper/2008.02268/) (2020)
- [Volume Rendering of Neural Implicit Surfaces](https://mlsanity.com/paper/2106.12052/) (2021)
- [PlenOctrees for Real-time Rendering of Neural Radiance Fields](https://mlsanity.com/paper/2103.14024/) (2021)
- [Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations](https://mlsanity.com/paper/1906.01618/) (2019)
- [Depth-supervised NeRF: Fewer Views and Faster Training for Free](https://mlsanity.com/paper/2107.02791/) (2021)
- [Block-NeRF: Scalable Large Scene Neural View Synthesis](https://mlsanity.com/paper/2202.05263/) (2022)
- [Nerfstudio: A Modular Framework for Neural Radiance Field Development](https://mlsanity.com/paper/2302.04264/) (2023)
- [MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstruction](https://mlsanity.com/paper/2206.00665/) (2022)
- [Neuralangelo: High-Fidelity Neural Surface Reconstruction](https://mlsanity.com/paper/2306.03092/) (2023)
- [DeepVoxels: Learning Persistent 3D Feature Embeddings](https://mlsanity.com/paper/1812.01024/) (2018)
- [Neural RGB-D Surface Reconstruction](https://mlsanity.com/paper/2104.04532/) (2021)
- [Urban Radiance Fields](https://mlsanity.com/paper/2111.14643/) (2021)
- [FAST-LIO2: Fast Direct LiDAR-inertial Odometry](https://mlsanity.com/paper/2107.06829/) (2021)
- [R3LIVE: A Robust, Real-time, RGB-colored, LiDAR-Inertial-Visual tightly-coupled state Estimation and mapping package](https://mlsanity.com/paper/2109.07982/) (2021)
- [SHINE-Mapping: Large-Scale 3D Mapping Using Sparse Hierarchical Implicit Neural Representations](https://mlsanity.com/paper/2210.02299/) (2022)
- [NeRF-LOAM: Neural Implicit Representation for Large-Scale Incremental LiDAR Odometry and Mapping](https://mlsanity.com/paper/2303.10709/) (2023)
- [Voxgraph: Globally Consistent, Volumetric Mapping using Signed Distance Function Submaps](https://mlsanity.com/paper/2004.13154/) (2020)
- [Super Odometry: IMU-centric LiDAR-Visual-Inertial Estimator for Challenging Environments](https://mlsanity.com/paper/2104.14938/) (2021)
- [VILENS: Visual, Inertial, Lidar, and Leg Odometry for All-Terrain Legged Robots](https://mlsanity.com/paper/2107.07243/) (2021)
- [3D Lidar Reconstruction with Probabilistic Depth Completion for Robotic Navigation](https://mlsanity.com/paper/2207.12520/) (2022)

## Built on by (most cited first, 14 of 14)

- [What Is The Best 3D Scene Representation for Robotics? From Geometric to Foundation Models](https://mlsanity.com/paper/2512.03422/) (2025)
- [PINGS: Gaussian Splatting Meets Distance Fields within a Point-Based Implicit Neural Map](https://mlsanity.com/paper/2502.05752/) (2025)
- [GS-SDF: LiDAR-Augmented Gaussian Splatting and Neural SDF for Geometrically Consistent Rendering and Reconstruction](https://mlsanity.com/paper/2503.10170/) (2025)
- [NeuraLoc: Visual Localization in Neural Implicit Map with Dual Complementary Features](https://mlsanity.com/paper/2503.06117/) (2025)
- [LiDAR-VGGT: Cross-Modal Coarse-to-Fine Fusion for Globally Consistent and Metric-Scale Dense Mapping](https://mlsanity.com/paper/2511.01186/) (2025)
- [Visual Localization in 3D Maps: Comparing Point Cloud, Mesh, and NeRF Representations](https://mlsanity.com/paper/2408.11966/) (2024)
- [Neural Surface Reconstruction and Rendering for LiDAR-Visual Systems](https://mlsanity.com/paper/2409.05310/) (2024)
- [SiLVR: Scalable Lidar-Visual Radiance Field Reconstruction with Uncertainty Quantification](https://mlsanity.com/paper/2502.02657/) (2025)
- [Tile and Slide : A New Framework for Scaling NeRF from Local to Global 3D Earth Observation](https://mlsanity.com/paper/2507.01631/) (2025)
- [Neural Implicit Representation for Highly Dynamic LiDAR Mapping and Odometry](https://mlsanity.com/paper/2409.17729/) (2024)
- [LiVisSfM: Accurate and Robust Structure-from-Motion with LiDAR and Visual Cues](https://mlsanity.com/paper/2410.22213/) (2024)
- [DQO-MAP: Dual Quadrics Multi-Object mapping with Gaussian Splatting](https://mlsanity.com/paper/2503.02223/) (2025)
- [NVSim: Novel View Synthesis Simulator for Large Scale Indoor Navigation](https://mlsanity.com/paper/2510.24335/) (2025)
- [LAPS: Improving Incremental LiDAR Mapping using Active Pooling and Sampling for Neural Distance Fields](https://mlsanity.com/paper/2605.15496/) (2026)

## Abstract (arXiv)

We present a neural-field-based large-scale reconstruction system that fuses lidar and vision data to generate high-quality reconstructions that are geometrically accurate and capture photo-realistic textures. This system adapts the state-of-the-art neural radiance field (NeRF) representation to also incorporate lidar data which adds strong geometric constraints on the depth and surface normals. We exploit the trajectory from a real-time lidar SLAM system to bootstrap a Structure-from-Motion (SfM) procedure to both significantly reduce the computation time and to provide metric scale which is crucial for lidar depth loss. We use submapping to scale the system to large-scale environments captured over long trajectories. We demonstrate the reconstruction system with data from a multi-camera, lidar sensor suite onboard a legged robot, hand-held while scanning building scenes for 600 metres, and onboard an aerial robot surveying a multi-storey mock disaster site-building. Website: https://ori-drs.github.io/projects/silvr/