# Surface-Based Visibility-Guided Uncertainty for Continuous Active 3D Neural Reconstruction

arXiv: https://arxiv.org/abs/2405.02568 · HTML: https://mlsanity.com/paper/2405.02568/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 44 references that are in the map.
- First posted on arXiv 4 May 2024.

## Builds on (most cited first, 22 of 22)

- [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)
- [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)
- [NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction](https://mlsanity.com/paper/2106.10689/) (2021)
- [2D Gaussian Splatting for Geometrically Accurate Radiance Fields](https://mlsanity.com/paper/2403.17888/) (2024)
- [Neural Sparse Voxel Fields](https://mlsanity.com/paper/2007.11571/) (2020)
- [Implicit Geometric Regularization for Learning Shapes](https://mlsanity.com/paper/2002.10099/) (2020)
- [SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Rendering](https://mlsanity.com/paper/2311.12775/) (2023)
- [BlendedMVS: A Large-scale Dataset for Generalized Multi-view Stereo Networks](https://mlsanity.com/paper/1911.10127/) (2019)
- [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)
- [NeuS2: Fast Learning of Neural Implicit Surfaces for Multi-view Reconstruction](https://mlsanity.com/paper/2212.05231/) (2022)
- [FreeNeRF: Improving Few-shot Neural Rendering with Free Frequency Regularization](https://mlsanity.com/paper/2303.07418/) (2023)
- [High-quality Surface Reconstruction using Gaussian Surfels](https://mlsanity.com/paper/2404.17774/) (2024)
- [NeuSG: Neural Implicit Surface Reconstruction with 3D Gaussian Splatting Guidance](https://mlsanity.com/paper/2312.00846/) (2023)
- [ActiveNeRF: Learning where to See with Uncertainty Estimation](https://mlsanity.com/paper/2209.08546/) (2022)
- [FisherRF: Active View Selection and Uncertainty Quantification for Radiance Fields using Fisher Information](https://mlsanity.com/paper/2311.17874/) (2023)
- [NeU-NBV: Next Best View Planning Using Uncertainty Estimation in Image-Based Neural Rendering](https://mlsanity.com/paper/2303.01284/) (2023)
- [ActiveRMAP: Radiance Field for Active Mapping And Planning](https://mlsanity.com/paper/2211.12656/) (2022)
- [NARUTO: Neural Active Reconstruction from Uncertain Target Observations](https://mlsanity.com/paper/2402.18771/) (2024)
- [Floaters No More: Radiance Field Gradient Scaling for Improved Near-Camera Training](https://mlsanity.com/paper/2305.02756/) (2023)

## Abstract (arXiv)

View selection is critical in active 3D neural reconstruction as it impacts the contents of training set and resulting final output quality. Recent view selection strategies emphasize the visibility when evaluating model uncertainty in active 3D reconstruction. However, existing approaches estimate visibility only after the model fully converges, which has confined their application primarily to non-continuous active learning settings. This paper proposes Surface-Based Visibility field (SBV) that successfully estimates the visibility-guided uncertainty in continuous active 3D neural reconstruction. During learning neural implicit surfaces, our model learns rendering uncertainties and infers surface confidence values derived from signed distance functions. It then updates surface confidences using a voxel grid, robustly deducing the surface-based visibility for uncertainties. This approach captures uncertainties across all regions, whether well-defined surfaces or ambiguous areas, ensuring accurate visibility measurement in continuous active learning. Experiments on benchmark datasets-Tanks and Temples, BlendedMVS, Blender, DTU-and the newly proposed imbalanced viewpoint dataset (ImBView) show that view selection based on SBV-guided uncertainty improves performance by up to 11.6% over existing methods, highlighting its effectiveness in challenging reconstruction scenarios.