Sources of Uncertainty in 3D Scene Reconstruction

• URL: http://arxiv.org/abs/2409.06407v1
• Date: Tue, 10 Sep 2024 10:43:53 GMT
• Title: Sources of Uncertainty in 3D Scene Reconstruction
• Authors: Marcus Klasson, Riccardo Mereu, Juho Kannala, Arno Solin,
• Abstract summary: We introduce a taxonomy that categorizes different sources of uncertainty inherent in 3D scene reconstruction methods. We extend NeRF- and GS-based methods with uncertainty estimation techniques, including learning uncertainty outputs and ensembles. Our study highlights the need for addressing various uncertainty aspects when designing NeRF/GS-based methods for uncertainty-aware 3D reconstruction.
• Score: 19.807599821939633
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The process of 3D scene reconstruction can be affected by numerous uncertainty sources in real-world scenes. While Neural Radiance Fields (NeRFs) and 3D Gaussian Splatting (GS) achieve high-fidelity rendering, they lack built-in mechanisms to directly address or quantify uncertainties arising from the presence of noise, occlusions, confounding outliers, and imprecise camera pose inputs. In this paper, we introduce a taxonomy that categorizes different sources of uncertainty inherent in these methods. Moreover, we extend NeRF- and GS-based methods with uncertainty estimation techniques, including learning uncertainty outputs and ensembles, and perform an empirical study to assess their ability to capture the sensitivity of the reconstruction. Our study highlights the need for addressing various uncertainty aspects when designing NeRF/GS-based methods for uncertainty-aware 3D reconstruction.

Related papers

• PF3plat: Pose-Free Feed-Forward 3D Gaussian Splatting [54.7468067660037]
  PF3plat sets a new state-of-the-art across all benchmarks, supported by comprehensive ablation studies validating our design choices. Our framework capitalizes on fast speed, scalability, and high-quality 3D reconstruction and view synthesis capabilities of 3DGS.
  arXiv  Detail & Related papers  (2024-10-29T15:28:15Z)
• Deep Modeling of Non-Gaussian Aleatoric Uncertainty [4.969887562291159]
  Deep learning offers promising new ways to accurately model aleatoric uncertainty in robotic estimation systems. In this study, we formulate and evaluate three fundamental deep learning approaches for conditional probability density modeling. Our results show that these deep learning methods can accurately capture complex uncertainty patterns, highlighting their potential for improving the reliability and robustness of estimation systems.
  arXiv  Detail & Related papers  (2024-05-30T22:13:17Z)
• Modeling uncertainty for Gaussian Splatting [21.836830270709]
  We present the first framework for uncertainty estimation using Gaussian Splatting (GS) We introduce a Variational Inference-based approach that seamlessly integrates uncertainty prediction into the common rendering pipeline of GS. We also introduce the Area Under Sparsification Error (AUSE) as a new term in the loss function, enabling optimization of uncertainty estimation alongside image reconstruction.
  arXiv  Detail & Related papers  (2024-03-27T11:45:08Z)
• Calib3D: Calibrating Model Preferences for Reliable 3D Scene Understanding [55.32861154245772]
  Calib3D is a pioneering effort to benchmark and scrutinize the reliability of 3D scene understanding models. We evaluate 28 state-of-the-art models across 10 diverse 3D datasets. We introduce DeptS, a novel depth-aware scaling approach aimed at enhancing 3D model calibration.
  arXiv  Detail & Related papers  (2024-03-25T17:59:59Z)
• Instant Uncertainty Calibration of NeRFs Using a Meta-calibrator [60.47106421809998]
  We introduce the concept of a meta-calibrator that performs uncertainty calibration for NeRFs with a single forward pass. We show that the meta-calibrator can generalize on unseen scenes and achieves well-calibrated and state-of-the-art uncertainty for NeRFs.
  arXiv  Detail & Related papers  (2023-12-04T21:29:31Z)
• Estimating 3D Uncertainty Field: Quantifying Uncertainty for Neural Radiance Fields [25.300284510832974]
  We propose a novel approach to estimate a 3D Uncertainty Field based on the learned incomplete scene geometry. By considering the accumulated transmittance along each camera ray, our Uncertainty Field infers 2D pixel-wise uncertainty. Our experiments demonstrate that our approach is the only one that can explicitly reason about high uncertainty both on 3D unseen regions and its involved 2D rendered pixels.
  arXiv  Detail & Related papers  (2023-11-03T09:47:53Z)
• Leveraging Neural Radiance Fields for Uncertainty-Aware Visual Localization [56.95046107046027]
  We propose to leverage Neural Radiance Fields (NeRF) to generate training samples for scene coordinate regression. Despite NeRF's efficiency in rendering, many of the rendered data are polluted by artifacts or only contain minimal information gain.
  arXiv  Detail & Related papers  (2023-10-10T20:11:13Z)
• Density-aware NeRF Ensembles: Quantifying Predictive Uncertainty in Neural Radiance Fields [7.380217868660371]
  We show that ensembling effectively quantifies model uncertainty in Neural Radiance Fields (NeRFs) We demonstrate that NeRF uncertainty can be utilised for next-best view selection and model refinement.
  arXiv  Detail & Related papers  (2022-09-19T02:28:33Z)
• The Unreasonable Effectiveness of Deep Evidential Regression [72.30888739450343]
  A new approach with uncertainty-aware regression-based neural networks (NNs) shows promise over traditional deterministic methods and typical Bayesian NNs. We detail the theoretical shortcomings and analyze the performance on synthetic and real-world data sets, showing that Deep Evidential Regression is a quantification rather than an exact uncertainty.
  arXiv  Detail & Related papers  (2022-05-20T10:10:32Z)
• Quantifying Sources of Uncertainty in Deep Learning-Based Image Reconstruction [5.129343375966527]
  We propose a scalable and efficient framework to simultaneously quantify aleatoric and epistemic uncertainties in learned iterative image reconstruction. We show that our method exhibits competitive performance against conventional benchmarks for computed tomography with both sparse view and limited angle data.
  arXiv  Detail & Related papers  (2020-11-17T04:12:52Z)
• Localization Uncertainty Estimation for Anchor-Free Object Detection [48.931731695431374]
  There are several limitations of the existing uncertainty estimation methods for anchor-based object detection. We propose a new localization uncertainty estimation method called UAD for anchor-free object detection. Our method captures the uncertainty in four directions of box offsets that are homogeneous, so that it can tell which direction is uncertain.
  arXiv  Detail & Related papers  (2020-06-28T13:49:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.