UC-NeRF: Uncertainty-aware Conditional Neural Radiance Fields from Endoscopic Sparse Views

• URL: http://arxiv.org/abs/2409.02917v2
• Date: Sat, 09 Nov 2024 15:33:32 GMT
• Title: UC-NeRF: Uncertainty-aware Conditional Neural Radiance Fields from Endoscopic Sparse Views
• Abstract summary: Novel View Synthesis offers geometry and appearance reconstruction, enhancing understanding, planning, and decision-making in surgical scenes. NeRF's direct application to surgical scenes produces unsatisfying results due to endoscopic sparse views and significant photometric inconsistencies. We propose uncertainty-aware conditional NeRF for novel view synthesis to tackle the severe shape-radiance ambiguity from sparse surgical views.
• Score: 43.367005002662154
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visualizing surgical scenes is crucial for revealing internal anatomical structures during minimally invasive procedures. Novel View Synthesis is a vital technique that offers geometry and appearance reconstruction, enhancing understanding, planning, and decision-making in surgical scenes. Despite the impressive achievements of Neural Radiance Field (NeRF), its direct application to surgical scenes produces unsatisfying results due to two challenges: endoscopic sparse views and significant photometric inconsistencies. In this paper, we propose uncertainty-aware conditional NeRF for novel view synthesis to tackle the severe shape-radiance ambiguity from sparse surgical views. The core of UC-NeRF is to incorporate the multi-view uncertainty estimation to condition the neural radiance field for modeling the severe photometric inconsistencies adaptively. Specifically, our UC-NeRF first builds a consistency learner in the form of multi-view stereo network, to establish the geometric correspondence from sparse views and generate uncertainty estimation and feature priors. In neural rendering, we design a base-adaptive NeRF network to exploit the uncertainty estimation for explicitly handling the photometric inconsistencies. Furthermore, an uncertainty-guided geometry distillation is employed to enhance geometry learning. Experiments on the SCARED and Hamlyn datasets demonstrate our superior performance in rendering appearance and geometry, consistently outperforming the current state-of-the-art approaches. Our code will be released at https://github.com/wrld/UC-NeRF.

Related papers

• Endo-NeRF++: Uncertainty-Aware Neural Rendering with Multi-Resolution Hash Encoding for Dynamic Surgical Scene Reconstruction [0.0]
  Reconstructing dynamic surgical scenes is crucial for robot-assisted minimally invasive surgery.<n>We introduce Endo-NeRF++, a neural rendering framework that accounts for uncertainty in the reconstruction of dynamic surgical scenes.
  arXiv  Detail & Related papers  (2026-07-30T08:03:52Z)
• Learning neural representations for X-ray ptychography reconstruction with unknown probes [9.112462327278404]
  X-ray ptychography provides exceptional nanoscale resolution and is widely applied in materials science, biology, and nanotechnology.<n>Its full potential is constrained by the critical challenge of accurately reconstructing images when the probe is unknown.<n>Conventional iterative methods and deep learning approaches are often suboptimal, particularly under the low-signal conditions inherent to low-dose and high-speed experiments.<n>In this work, we introduce the Ptychographic Implicit Neural Representation (PvtINR), a self-supervised framework that simultaneously addresses the object and probe recovery problem.
  arXiv  Detail & Related papers  (2025-09-04T17:13:19Z)
• Synomaly Noise and Multi-Stage Diffusion: A Novel Approach for Unsupervised Anomaly Detection in Ultrasound Imaging [32.99597899937902]
  We propose a novel unsupervised anomaly detection framework based on a diffusion model. The proposed framework incorporates a synthetic anomaly (Synomaly) noise function and a multi-stage diffusion process. We validate the proposed approach on carotid US, brain MRI, and liver CT datasets.
  arXiv  Detail & Related papers  (2024-11-06T15:43:51Z)
• K-Space-Aware Cross-Modality Score for Synthesized Neuroimage Quality Assessment [71.27193056354741]
  The problem of how to assess cross-modality medical image synthesis has been largely unexplored. We propose a new metric K-CROSS to spur progress on this challenging problem. K-CROSS uses a pre-trained multi-modality segmentation network to predict the lesion location.
  arXiv  Detail & Related papers  (2023-07-10T01:26:48Z)
• NeRFVS: Neural Radiance Fields for Free View Synthesis via Geometry Scaffolds [60.1382112938132]
  We present NeRFVS, a novel neural radiance fields (NeRF) based method to enable free navigation in a room. NeRF achieves impressive performance in rendering images for novel views similar to the input views while suffering for novel views that are significantly different from the training views.
  arXiv  Detail & Related papers  (2023-04-13T06:40:08Z)
• Clean-NeRF: Reformulating NeRF to account for View-Dependent Observations [67.54358911994967]
  This paper proposes Clean-NeRF for accurate 3D reconstruction and novel view rendering in complex scenes. Clean-NeRF can be implemented as a plug-in that can immediately benefit existing NeRF-based methods without additional input.
  arXiv  Detail & Related papers  (2023-03-26T12:24:31Z)
• Self-NeRF: A Self-Training Pipeline for Few-Shot Neural Radiance Fields [17.725937326348994]
  We propose Self-NeRF, a self-evolved NeRF that iteratively refines the radiance fields with very few number of input views. In each iteration, we label unseen views with the predicted colors or warped pixels generated by the model from the preceding iteration. These expanded pseudo-views are afflicted by imprecision in color and warping artifacts, which degrades the performance of NeRF.
  arXiv  Detail & Related papers  (2023-03-10T08:22:36Z)
• GeCoNeRF: Few-shot Neural Radiance Fields via Geometric Consistency [31.22435282922934]
  We present a novel framework to regularize Neural Radiance Field (NeRF) in a few-shot setting with a geometry-aware consistency regularization. We show that our model achieves competitive results compared to state-of-the-art few-shot NeRF models.
  arXiv  Detail & Related papers  (2023-01-26T05:14:12Z)
• SiNeRF: Sinusoidal Neural Radiance Fields for Joint Pose Estimation and Scene Reconstruction [147.9379707578091]
  NeRFmm is the Neural Radiance Fields (NeRF) that deal with Joint Optimization tasks. Despite NeRFmm producing precise scene synthesis and pose estimations, it still struggles to outperform the full-annotated baseline on challenging scenes. We propose Sinusoidal Neural Radiance Fields (SiNeRF) that leverage sinusoidal activations for radiance mapping and a novel Mixed Region Sampling (MRS) for selecting ray batch efficiently.
  arXiv  Detail & Related papers  (2022-10-10T10:47:51Z)
• InfoNeRF: Ray Entropy Minimization for Few-Shot Neural Volume Rendering [55.70938412352287]
  We present an information-theoretic regularization technique for few-shot novel view synthesis based on neural implicit representation. The proposed approach minimizes potential reconstruction inconsistency that happens due to insufficient viewpoints. We achieve consistently improved performance compared to existing neural view synthesis methods by large margins on multiple standard benchmarks.
  arXiv  Detail & Related papers  (2021-12-31T11:56:01Z)

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.