# SCADE: NeRFs from Space Carving with Ambiguity-Aware Depth Estimates

arXiv: https://arxiv.org/abs/2303.13582 · HTML: https://mlsanity.com/paper/2303.13582/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 42 of its 59 references that are in the map.
- First posted on arXiv 23 Mar 2023.

## Builds on (most cited first, 30 of 42)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](https://mlsanity.com/paper/2006.10739/) (2020)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction](https://mlsanity.com/paper/2106.10689/) (2021)
- [pixelNeRF: Neural Radiance Fields from One or Few Images](https://mlsanity.com/paper/2012.02190/) (2020)
- [Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer](https://mlsanity.com/paper/1907.01341/) (2019)
- [Depth Map Prediction from a Single Image using a Multi-Scale Deep Network](https://mlsanity.com/paper/1406.2283/) (2014)
- [NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections](https://mlsanity.com/paper/2008.02268/) (2020)
- [Nerfies: Deformable Neural Radiance Fields](https://mlsanity.com/paper/2011.12948/) (2020)
- [Volume Rendering of Neural Implicit Surfaces](https://mlsanity.com/paper/2106.12052/) (2021)
- [Unsupervised Monocular Depth Estimation with Left-Right Consistency](https://mlsanity.com/paper/1609.03677/) (2016)
- [Unsupervised Learning of Depth and Ego-Motion from Video](https://mlsanity.com/paper/1704.07813/) (2017)
- [Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes](https://mlsanity.com/paper/2011.13084/) (2020)
- [MVSNeRF: Fast Generalizable Radiance Field Reconstruction from Multi-View Stereo](https://mlsanity.com/paper/2103.15595/) (2021)
- [MegaDepth: Learning Single-View Depth Prediction from Internet Photos](https://mlsanity.com/paper/1804.00607/) (2018)
- [Depth-supervised NeRF: Fewer Views and Faster Training for Free](https://mlsanity.com/paper/2107.02791/) (2021)
- [IBRNet: Learning Multi-View Image-Based Rendering](https://mlsanity.com/paper/2102.13090/) (2021)
- [UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstruction](https://mlsanity.com/paper/2104.10078/) (2021)
- [Taskonomy: Disentangling Task Transfer Learning](https://mlsanity.com/paper/1804.08328/) (2018)
- [RegNeRF: Regularizing Neural Radiance Fields for View Synthesis from Sparse Inputs](https://mlsanity.com/paper/2112.00724/) (2021)
- [Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fields](https://mlsanity.com/paper/2112.03907/) (2021)
- [MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstruction](https://mlsanity.com/paper/2206.00665/) (2022)
- [Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesis](https://mlsanity.com/paper/2104.00677/) (2021)
- [Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matching](https://mlsanity.com/paper/1912.06378/) (2019)

## Built on by (most cited first, 30 of 35)

- [ReconFusion: 3D Reconstruction with Diffusion Priors](https://mlsanity.com/paper/2312.02981/) (2023)
- [DNGaussian: Optimizing Sparse-View 3D Gaussian Radiance Fields with Global-Local Depth Normalization](https://mlsanity.com/paper/2403.06912/) (2024)
- [SimpleNeRF: Regularizing Sparse Input Neural Radiance Fields with Simpler Solutions](https://mlsanity.com/paper/2309.03955/) (2023)
- [MVPGS: Excavating Multi-view Priors for Gaussian Splatting from Sparse Input Views](https://mlsanity.com/paper/2409.14316/) (2024)
- [DaRF: Boosting Radiance Fields from Sparse Inputs with Monocular Depth Adaptation](https://mlsanity.com/paper/2305.19201/) (2023)
- [FrugalNeRF: Fast Convergence for Extreme Few-shot Novel View Synthesis without Learned Priors](https://mlsanity.com/paper/2410.16271/) (2024)
- [BoostMVSNeRFs: Boosting MVS-based NeRFs to Generalizable View Synthesis in Large-scale Scenes](https://mlsanity.com/paper/2407.15848/) (2024)
- [Factorized Motion Fields for Fast Sparse Input Dynamic View Synthesis](https://mlsanity.com/paper/2404.11669/) (2024)
- [NeRF Revisited: Fixing Quadrature Instability in Volume Rendering](https://mlsanity.com/paper/2310.20685/) (2023)
- [UMCFuse: A Unified Multiple Complex Scenes Infrared and Visible Image Fusion Framework](https://mlsanity.com/paper/2402.02096/) (2024)
- [Semantically-aware Neural Radiance Fields for Visual Scene Understanding: A Comprehensive Review](https://mlsanity.com/paper/2402.11141/) (2024)
- [DoubleTake: Geometry Guided Depth Estimation](https://mlsanity.com/paper/2406.18387/) (2024)
- [View-Consistent Hierarchical 3D Segmentation Using Ultrametric Feature Fields](https://mlsanity.com/paper/2405.19678/) (2024)
- [Uncertainty-guided Optimal Transport in Depth Supervised Sparse-View 3D Gaussian](https://mlsanity.com/paper/2405.19657/) (2024)
- [Few-shot NeRF by Adaptive Rendering Loss Regularization](https://mlsanity.com/paper/2410.17839/) (2024)
- [G3DR: Generative 3D Reconstruction in ImageNet](https://mlsanity.com/paper/2403.00939/) (2024)
- [SAID-NeRF: Segmentation-AIDed NeRF for Depth Completion of Transparent Objects](https://mlsanity.com/paper/2403.19607/) (2024)
- [Geometric Algebra Planes: Convex Implicit Neural Volumes](https://mlsanity.com/paper/2411.13525/) (2024)
- [UFV-Splatter: Pose-Free Feed-Forward 3D Gaussian Splatting Adapted to Unfavorable Views](https://mlsanity.com/paper/2507.22342/) (2025)
- [ProvNeRF: Modeling per Point Provenance in NeRFs as a Stochastic Field](https://mlsanity.com/paper/2401.08140/) (2024)
- [Towards a Robust Framework for NeRF Evaluation](https://mlsanity.com/paper/2305.18079/) (2023)
- [Reducing Shape-Radiance Ambiguity in Radiance Fields with a Closed-Form Color Estimation Method](https://mlsanity.com/paper/2312.12726/) (2023)
- [Depth-guided NeRF Training via Earth Mover's Distance](https://mlsanity.com/paper/2403.13206/) (2024)
- [MomentsNeRF: Leveraging Orthogonal Moments for Few-Shot Neural Rendering](https://mlsanity.com/paper/2407.02668/) (2024)
- [Stable Surface Regularization for Fast Few-Shot NeRF](https://mlsanity.com/paper/2403.19985/) (2024)
- [Simple-RF: Regularizing Sparse Input Radiance Fields with Simpler Solutions](https://mlsanity.com/paper/2404.19015/) (2024)
- [SemiDDM-Weather: A Semi-supervised Learning Framework for All-in-one Adverse Weather Removal](https://mlsanity.com/paper/2409.19679/) (2024)
- [RDG-GS: Relative Depth Guidance with Gaussian Splatting for Real-time Sparse-View 3D Rendering](https://mlsanity.com/paper/2501.11102/) (2025)
- [A View-consistent Sampling Method for Regularized Training of Neural Radiance Fields](https://mlsanity.com/paper/2507.04408/) (2025)
- [LighthouseGS: Indoor Structure-aware 3D Gaussian Splatting for Panorama-Style Mobile Captures](https://mlsanity.com/paper/2507.06109/) (2025)

## Abstract (arXiv)

Neural radiance fields (NeRFs) have enabled high fidelity 3D reconstruction from multiple 2D input views. However, a well-known drawback of NeRFs is the less-than-ideal performance under a small number of views, due to insufficient constraints enforced by volumetric rendering. To address this issue, we introduce SCADE, a novel technique that improves NeRF reconstruction quality on sparse, unconstrained input views for in-the-wild indoor scenes. To constrain NeRF reconstruction, we leverage geometric priors in the form of per-view depth estimates produced with state-of-the-art monocular depth estimation models, which can generalize across scenes. A key challenge is that monocular depth estimation is an ill-posed problem, with inherent ambiguities. To handle this issue, we propose a new method that learns to predict, for each view, a continuous, multimodal distribution of depth estimates using conditional Implicit Maximum Likelihood Estimation (cIMLE). In order to disambiguate exploiting multiple views, we introduce an original space carving loss that guides the NeRF representation to fuse multiple hypothesized depth maps from each view and distill from them a common geometry that is consistent with all views. Experiments show that our approach enables higher fidelity novel view synthesis from sparse views. Our project page can be found at https://scade-spacecarving-nerfs.github.io .