# SC-NeRF: Self-Correcting Neural Radiance Field with Sparse Views

arXiv: https://arxiv.org/abs/2309.05028 · HTML: https://mlsanity.com/paper/2309.05028/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 43 references that are in the map.
- First posted on arXiv 10 Sep 2023.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [pixelNeRF: Neural Radiance Fields from One or Few Images](https://mlsanity.com/paper/2012.02190/) (2020)
- [NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections](https://mlsanity.com/paper/2008.02268/) (2020)
- [Neural Sparse Voxel Fields](https://mlsanity.com/paper/2007.11571/) (2020)
- [MVSNet: Depth Inference for Unstructured Multi-view Stereo](https://mlsanity.com/paper/1804.02505/) (2018)
- [MVSNeRF: Fast Generalizable Radiance Field Reconstruction from Multi-View Stereo](https://mlsanity.com/paper/2103.15595/) (2021)
- [IBRNet: Learning Multi-View Image-Based Rendering](https://mlsanity.com/paper/2102.13090/) (2021)
- [Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelines](https://mlsanity.com/paper/1905.00889/) (2019)
- [Common Objects in 3D: Large-Scale Learning and Evaluation of Real-life 3D Category Reconstruction](https://mlsanity.com/paper/2109.00512/) (2021)
- [Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matching](https://mlsanity.com/paper/1912.06378/) (2019)
- [Recurrent MVSNet for High-resolution Multi-view Stereo Depth Inference](https://mlsanity.com/paper/1902.10556/) (2019)
- [DeepView: View Synthesis with Learned Gradient Descent](https://mlsanity.com/paper/1906.07316/) (2019)
- [Depth-Aware Video Frame Interpolation](https://mlsanity.com/paper/1904.00830/) (2019)
- [Neural Rays for Occlusion-aware Image-based Rendering](https://mlsanity.com/paper/2107.13421/) (2021)
- [Stereo Radiance Fields (SRF): Learning View Synthesis for Sparse Views of Novel Scenes](https://mlsanity.com/paper/2104.06935/) (2021)
- [GRF: Learning a General Radiance Field for 3D Representation and Rendering](https://mlsanity.com/paper/2010.04595/) (2020)
- [Point-Based Multi-View Stereo Network](https://mlsanity.com/paper/1908.04422/) (2019)
- [Generalizable Patch-Based Neural Rendering](https://mlsanity.com/paper/2207.10662/) (2022)
- [Is Attention All That NeRF Needs?](https://mlsanity.com/paper/2207.13298/) (2022)
- [NeRFLiX: High-Quality Neural View Synthesis by Learning a Degradation-Driven Inter-viewpoint MiXer](https://mlsanity.com/paper/2303.06919/) (2023)
- [Efficient 3D Deep LiDAR Odometry](https://mlsanity.com/paper/2111.02135/) (2021)
- [Unsupervised Learning of Depth, Optical Flow and Pose with Occlusion from 3D Geometry](https://mlsanity.com/paper/2003.00766/) (2020)
- [Improving Neural Radiance Fields with Depth-aware Optimization for Novel View Synthesis](https://mlsanity.com/paper/2304.05218/) (2023)

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

- [SMERF: Streamable Memory Efficient Radiance Fields for Real-Time Large-Scene Exploration](https://mlsanity.com/paper/2312.07541/) (2023)
- [StreamGS: Online Generalizable Gaussian Splatting Reconstruction for Unposed Image Streams](https://mlsanity.com/paper/2503.06235/) (2025)
- [Semantically-aware Neural Radiance Fields for Visual Scene Understanding: A Comprehensive Review](https://mlsanity.com/paper/2402.11141/) (2024)
- [Robust and Efficient 3D Gaussian Splatting for Urban Scene Reconstruction](https://mlsanity.com/paper/2507.23006/) (2025)
- [GeoNLF: Geometry guided Pose-Free Neural LiDAR Fields](https://mlsanity.com/paper/2407.05597/) (2024)
- [RoGs: Large Scale Road Surface Reconstruction with Meshgrid Gaussian](https://mlsanity.com/paper/2405.14342/) (2024)
- [URS-NeRF: Unordered Rolling Shutter Bundle Adjustment for Neural Radiance Fields](https://mlsanity.com/paper/2403.10119/) (2024)
- [Sparse-View 3D Reconstruction: Recent Advances and Open Challenges](https://mlsanity.com/paper/2507.16406/) (2025)
- [Flow-NeRF: Joint Learning of Geometry, Poses, and Dense Flow within Unified Neural Representations](https://mlsanity.com/paper/2503.10464/) (2025)
- [AttentionGS: Towards Initialization-Free 3D Gaussian Splatting via Structural Attention](https://mlsanity.com/paper/2506.23611/) (2025)
- [Error-Driven Scene Editing for 3D Grounding in Large Language Models](https://mlsanity.com/paper/2511.14086/) (2025)
- [Gesplat: Robust Pose-Free 3D Reconstruction via Geometry-Guided Gaussian Splatting](https://mlsanity.com/paper/2510.10097/) (2025)
- [Dental3R: Geometry-Aware Pairing for Intraoral 3D Reconstruction from Sparse-View Photographs](https://mlsanity.com/paper/2511.14315/) (2025)
- [RoGS: Adaptive Meshgrid Gaussian for Large-Scale Road Surface Mapping](https://mlsanity.com/paper/2607.15048/) (2026)

## Abstract (arXiv)

In recent studies, the generalization of neural radiance fields for novel view synthesis task has been widely explored. However, existing methods are limited to objects and indoor scenes. In this work, we extend the generalization task to outdoor scenes, trained only on object-level datasets. This approach presents two challenges. Firstly, the significant distributional shift between training and testing scenes leads to black artifacts in rendering results. Secondly, viewpoint changes in outdoor scenes cause ghosting or missing regions in rendered images. To address these challenges, we propose a geometric correction module and an appearance correction module based on multi-head attention mechanisms. We normalize rendered depth and combine it with light direction as query in the attention mechanism. Our network effectively corrects varying scene structures and geometric features in outdoor scenes, generalizing well from object-level to unseen outdoor scenes. Additionally, we use appearance correction module to correct appearance features, preventing rendering artifacts like blank borders and ghosting due to viewpoint changes. By combining these modules, our approach successfully tackles the challenges of outdoor scene generalization, producing high-quality rendering results. When evaluated on four datasets (Blender, DTU, LLFF, Spaces), our network outperforms previous methods. Notably, compared to MVSNeRF, our network improves average PSNR from 19.369 to 25.989, SSIM from 0.838 to 0.889, and reduces LPIPS from 0.265 to 0.224 on Spaces outdoor scenes.