# NOPE-SAC: Neural One-Plane RANSAC for Sparse-View Planar 3D Reconstruction

arXiv: https://arxiv.org/abs/2211.16799 · HTML: https://mlsanity.com/paper/2211.16799/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 58 references that are in the map.
- First posted on arXiv 30 Nov 2022.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer](https://mlsanity.com/paper/1907.01341/) (2019)
- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [Matterport3D: Learning from RGB-D Data in Indoor Environments](https://mlsanity.com/paper/1709.06158/) (2017)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://mlsanity.com/paper/2107.06278/) (2021)
- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [LoFTR: Detector-Free Local Feature Matching with Transformers](https://mlsanity.com/paper/2104.00680/) (2021)
- [Computational Optimal Transport](https://mlsanity.com/paper/1803.00567/) (2018)
- [MegaDepth: Learning Single-View Depth Prediction from Internet Photos](https://mlsanity.com/paper/1804.00607/) (2018)
- [Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets from 3D Scans](https://mlsanity.com/paper/2110.04994/) (2021)
- [Enforcing geometric constraints of virtual normal for depth prediction](https://mlsanity.com/paper/1907.12209/) (2019)
- [Pixel-Perfect Structure-from-Motion with Featuremetric Refinement](https://mlsanity.com/paper/2108.08291/) (2021)
- [COTR: Correspondence Transformer for Matching Across Images](https://mlsanity.com/paper/2103.14167/) (2021)
- [ASpanFormer: Detector-Free Image Matching with Adaptive Span Transformer](https://mlsanity.com/paper/2208.14201/) (2022)
- [MatchFormer: Interleaving Attention in Transformers for Feature Matching](https://mlsanity.com/paper/2203.09645/) (2022)
- [PlaneRCNN: 3D Plane Detection and Reconstruction from a Single Image](https://mlsanity.com/paper/1812.04072/) (2018)
- [ContextDesc: Local Descriptor Augmentation with Cross-Modality Context](https://mlsanity.com/paper/1904.04084/) (2019)
- [PlaneNet: Piece-wise Planar Reconstruction from a Single RGB Image](https://mlsanity.com/paper/1804.06278/) (2018)
- [DeepSFM: Structure From Motion Via Deep Bundle Adjustment](https://mlsanity.com/paper/1912.09697/) (2019)
- [Single-Image Piece-wise Planar 3D Reconstruction via Associative Embedding](https://mlsanity.com/paper/1902.09777/) (2019)
- [Planar Surface Reconstruction from Sparse Views](https://mlsanity.com/paper/2103.14644/) (2021)
- [MonoIndoor: Towards Good Practice of Self-Supervised Monocular Depth Estimation for Indoor Environments](https://mlsanity.com/paper/2107.12429/) (2021)
- [PlaneTR: Structure-Guided Transformers for 3D Plane Recovery](https://mlsanity.com/paper/2107.13108/) (2021)
- [RPNet: an End-to-End Network for Relative Camera Pose Estimation](https://mlsanity.com/paper/1809.08402/) (2018)
- [PlaneFormers: From Sparse View Planes to 3D Reconstruction](https://mlsanity.com/paper/2208.04307/) (2022)
- [Associative3D: Volumetric Reconstruction from Sparse Views](https://mlsanity.com/paper/2007.13727/) (2020)
- [Improving Monocular Depth Estimation by Leveraging Structural Awareness and Complementary Datasets](https://mlsanity.com/paper/2007.11256/) (2020)

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

- [AirPlanes: Accurate Plane Estimation via 3D-Consistent Embeddings](https://mlsanity.com/paper/2406.08960/) (2024)
- [PlanarSplatting: Accurate Planar Surface Reconstruction in 3 Minutes](https://mlsanity.com/paper/2412.03451/) (2024)
- [NEAT: Distilling 3D Wireframes from Neural Attraction Fields](https://mlsanity.com/paper/2307.10206/) (2023)
- [FAR: Flexible, Accurate and Robust 6DoF Relative Camera Pose Estimation](https://mlsanity.com/paper/2403.03221/) (2024)
- [G4Splat: Geometry-Guided Gaussian Splatting with Generative Prior](https://mlsanity.com/paper/2510.12099/) (2025)
- [PlaneRecTR++: Unified Query Learning for Joint 3D Planar Reconstruction and Pose Estimation](https://mlsanity.com/paper/2307.13756/) (2023)
- [3DFIRES: Few Image 3D REconstruction for Scenes with Hidden Surface](https://mlsanity.com/paper/2403.08768/) (2024)
- [AlphaTablets: A Generic Plane Representation for 3D Planar Reconstruction from Monocular Videos](https://mlsanity.com/paper/2411.19950/) (2024)
- [Towards In-the-wild 3D Plane Reconstruction from a Single Image](https://mlsanity.com/paper/2506.02493/) (2025)
- [PlanarNeRF: Online Learning of Planar Primitives with Neural Radiance Fields](https://mlsanity.com/paper/2401.00871/) (2023)
- [SRPose: Two-view Relative Pose Estimation with Sparse Keypoints](https://mlsanity.com/paper/2407.08199/) (2024)
- [CityGo: Lightweight Urban Modeling and Rendering with Proxy Buildings and Residual Gaussians](https://mlsanity.com/paper/2505.21041/) (2025)
- [PLANA3R: Zero-shot Metric Planar 3D Reconstruction via Feed-Forward Planar Splatting](https://mlsanity.com/paper/2510.18714/) (2025)
- [RecGen3D: Reconstruction-Guided 3D Generation in a Shared Canonical Space](https://mlsanity.com/paper/2604.01479/) (2026)
- [Sparse-View 3D Reconstruction: Recent Advances and Open Challenges](https://mlsanity.com/paper/2507.16406/) (2025)
- [NOVA-3D: Non-overlapped Views for 3D Anime Character Reconstruction](https://mlsanity.com/paper/2405.12505/) (2024)
- [MonoPlane: Exploiting Monocular Geometric Cues for Generalizable 3D Plane Reconstruction](https://mlsanity.com/paper/2411.01226/) (2024)
- [Indoor 3D Reconstruction with an Unknown Camera-Projector Pair](https://mlsanity.com/paper/2407.01945/) (2024)
- [Interacted Planes Reveal 3D Line Mapping](https://mlsanity.com/paper/2602.01296/) (2026)
- [iGaussian: Real-Time Camera Pose Estimation via Feed-Forward 3D Gaussian Splatting Inversion](https://mlsanity.com/paper/2511.14149/) (2025)
- [PlanaReLoc: Camera Relocalization in 3D Planar Primitives via Region-Based Structure Matching](https://mlsanity.com/paper/2603.20818/) (2026)

## Abstract (arXiv)

This paper studies the challenging two-view 3D reconstruction in a rigorous sparse-view configuration, which is suffering from insufficient correspondences in the input image pairs for camera pose estimation. We present a novel Neural One-PlanE RANSAC framework (termed NOPE-SAC in short) that exerts excellent capability to learn one-plane pose hypotheses from 3D plane correspondences. Building on the top of a siamese plane detection network, our NOPE-SAC first generates putative plane correspondences with a coarse initial pose. It then feeds the learned 3D plane parameters of correspondences into shared MLPs to estimate the one-plane camera pose hypotheses, which are subsequently reweighed in a RANSAC manner to obtain the final camera pose. Because the neural one-plane pose minimizes the number of plane correspondences for adaptive pose hypotheses generation, it enables stable pose voting and reliable pose refinement in a few plane correspondences for the sparse-view inputs. In the experiments, we demonstrate that our NOPE-SAC significantly improves the camera pose estimation for the two-view inputs with severe viewpoint changes, setting several new state-of-the-art performances on two challenging benchmarks, i.e., MatterPort3D and ScanNet, for sparse-view 3D reconstruction. The source code is released at https://github.com/IceTTTb/NopeSAC for reproducible research.