# DReg-NeRF: Deep Registration for Neural Radiance Fields

arXiv: https://arxiv.org/abs/2308.09386 · HTML: https://mlsanity.com/paper/2308.09386/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 47 references that are in the map.
- First posted on arXiv 18 Aug 2023.

## Builds on (most cited first, 30 of 36)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [Plenoxels: Radiance Fields without Neural Networks](https://mlsanity.com/paper/2112.05131/) (2021)
- [Objaverse: A Universe of Annotated 3D Objects](https://mlsanity.com/paper/2212.08051/) (2022)
- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [TensoRF: Tensorial Radiance Fields](https://mlsanity.com/paper/2203.09517/) (2022)
- [KPConv: Flexible and Deformable Convolution for Point Clouds](https://mlsanity.com/paper/1904.08889/) (2019)
- [Neural Sparse Voxel Fields](https://mlsanity.com/paper/2007.11571/) (2020)
- [PlenOctrees for Real-time Rendering of Neural Radiance Fields](https://mlsanity.com/paper/2103.14024/) (2021)
- [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)
- [BARF: Bundle-Adjusting Neural Radiance Fields](https://mlsanity.com/paper/2104.06405/) (2021)
- [Mega-NeRF: Scalable Construction of Large-Scale NeRFs for Virtual Fly-Throughs](https://mlsanity.com/paper/2112.10703/) (2021)
- [INeRF: Inverting Neural Radiance Fields for Pose Estimation](https://mlsanity.com/paper/2012.05877/) (2020)
- [3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions](https://mlsanity.com/paper/1603.08182/) (2016)
- [Deep Closest Point: Learning Representations for Point Cloud Registration](https://mlsanity.com/paper/1905.03304/) (2019)
- [Dense Depth Priors for Neural Radiance Fields from Sparse Input Views](https://mlsanity.com/paper/2112.03288/) (2021)
- [PREDATOR: Registration of 3D Point Clouds with Low Overlap](https://mlsanity.com/paper/2011.13005/) (2020)
- [A General and Adaptive Robust Loss Function](https://mlsanity.com/paper/1701.03077/) (2017)
- [Pixel-Perfect Structure-from-Motion with Featuremetric Refinement](https://mlsanity.com/paper/2108.08291/) (2021)
- [RobustNeRF: Ignoring Distractors with Robust Losses](https://mlsanity.com/paper/2302.00833/) (2023)
- [REGTR: End-to-end Point Cloud Correspondences with Transformers](https://mlsanity.com/paper/2203.14517/) (2022)
- [NerfAcc: A General NeRF Acceleration Toolbox](https://mlsanity.com/paper/2210.04847/) (2022)
- [Segment Anything in 3D with Radiance Fields](https://mlsanity.com/paper/2304.12308/) (2023)
- [DBARF: Deep Bundle-Adjusting Generalizable Neural Radiance Fields](https://mlsanity.com/paper/2303.14478/) (2023)
- [nerf2nerf: Pairwise Registration of Neural Radiance Fields](https://mlsanity.com/paper/2211.01600/) (2022)

## Built on by (most cited first, 21 of 21)

- [LoopSplat: Loop Closure by Registering 3D Gaussian Splats](https://mlsanity.com/paper/2408.10154/) (2024)
- [DOGS: Distributed-Oriented Gaussian Splatting for Large-Scale 3D Reconstruction Via Gaussian Consensus](https://mlsanity.com/paper/2405.13943/) (2024)
- [GaussReg: Fast 3D Registration with Gaussian Splatting](https://mlsanity.com/paper/2407.05254/) (2024)
- [TrackNeRF: Bundle Adjusting NeRF from Sparse and Noisy Views via Feature Tracks](https://mlsanity.com/paper/2408.10739/) (2024)
- [ZeroGS: Training 3D Gaussian Splatting from Unposed Images](https://mlsanity.com/paper/2411.15779/) (2024)
- [NeRAF: 3D Scene Infused Neural Radiance and Acoustic Fields](https://mlsanity.com/paper/2405.18213/) (2024)
- [Radar Fields: An Extension of Radiance Fields to SAR](https://mlsanity.com/paper/2312.12961/) (2023)
- [SIREN: Semantic, Initialization-Free Registration of Multi-Robot Gaussian Splatting Maps](https://mlsanity.com/paper/2502.06519/) (2025)
- [C-NERF: Representing Scene Changes as Directional Consistency Difference-based NeRF](https://mlsanity.com/paper/2312.02751/) (2023)
- [Space-time 2D Gaussian Splatting for Accurate Surface Reconstruction under Complex Dynamic Scenes](https://mlsanity.com/paper/2409.18852/) (2024)
- [PhotoReg: Photometrically Registering 3D Gaussian Splatting Models](https://mlsanity.com/paper/2410.05044/) (2024)
- [SCALAR-NeRF: SCAlable LARge-scale Neural Radiance Fields for Scene Reconstruction](https://mlsanity.com/paper/2311.16657/) (2023)
- [Reg-NF: Efficient Registration of Implicit Surfaces within Neural Fields](https://mlsanity.com/paper/2402.09722/) (2024)
- [Holistic Large-Scale Scene Reconstruction via Mixed Gaussian Splatting](https://mlsanity.com/paper/2505.23280/) (2025)
- [Automated 3D-GS Registration and Fusion via Skeleton Alignment and Gaussian-Adaptive Features](https://mlsanity.com/paper/2507.20480/) (2025)
- [Registering Neural 4D Gaussians for Endoscopic Surgery](https://mlsanity.com/paper/2407.20213/) (2024)
- [VF-NeRF: Viewshed Fields for Rigid NeRF Registration](https://mlsanity.com/paper/2404.03349/) (2024)
- [FBINeRF: Feature-Based Integrated Recurrent Network for Pinhole and Fisheye Neural Radiance Fields](https://mlsanity.com/paper/2408.01878/) (2024)
- [GSCompleter: A Distillation-Free Plugin for Metric-Aware 3D Gaussian Splatting Completion in Seconds](https://mlsanity.com/paper/2604.20155/) (2026)
- [Cross-Instance Gaussian Splatting Registration via Geometry-Aware Feature-Guided Alignment](https://mlsanity.com/paper/2603.21936/) (2026)
- [GeGS-PCR: Effective and Robust 3D Point Cloud Registration with Two-Stage Color-Enhanced Geometric-3DGS Fusion](https://mlsanity.com/paper/2604.17721/) (2026)

## Abstract (arXiv)

Although Neural Radiance Fields (NeRF) is popular in the computer vision community recently, registering multiple NeRFs has yet to gain much attention. Unlike the existing work, NeRF2NeRF, which is based on traditional optimization methods and needs human annotated keypoints, we propose DReg-NeRF to solve the NeRF registration problem on object-centric scenes without human intervention. After training NeRF models, our DReg-NeRF first extracts features from the occupancy grid in NeRF. Subsequently, our DReg-NeRF utilizes a transformer architecture with self-attention and cross-attention layers to learn the relations between pairwise NeRF blocks. In contrast to state-of-the-art (SOTA) point cloud registration methods, the decoupled correspondences are supervised by surface fields without any ground truth overlapping labels. We construct a novel view synthesis dataset with 1,700+ 3D objects obtained from Objaverse to train our network. When evaluated on the test set, our proposed method beats the SOTA point cloud registration methods by a large margin, with a mean RPE=9.67circ and a mean RTE=0.038. Our code is available at https://github.com/AIBluefisher/DReg-NeRF.