PR-ENDO: Physically Based Relightable Gaussian Splatting for Endoscopy
- URL: http://arxiv.org/abs/2411.12510v1
- Date: Tue, 19 Nov 2024 13:52:30 GMT
- Title: PR-ENDO: Physically Based Relightable Gaussian Splatting for Endoscopy
- Authors: Joanna Kaleta, Weronika Smolak-Dyżewska, Dawid Malarz, Diego Dall'Alba, Przemysław Korzeniowski, Przemysław Spurek,
- Abstract summary: We present PR-ENDO, a framework that leverages 3D Splatting within a physically based, relightable model tailored for the complex acquisition conditions in endoscopy.
Our methods demonstrated superior image quality compared to baseline approaches.
- Score: 1.28795255913358
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Endoscopic procedures are crucial for colorectal cancer diagnosis, and three-dimensional reconstruction of the environment for real-time novel-view synthesis can significantly enhance diagnosis. We present PR-ENDO, a framework that leverages 3D Gaussian Splatting within a physically based, relightable model tailored for the complex acquisition conditions in endoscopy, such as restricted camera rotations and strong view-dependent illumination. By exploiting the connection between the camera and light source, our approach introduces a relighting model to capture the intricate interactions between light and tissue using physically based rendering and MLP. Existing methods often produce artifacts and inconsistencies under these conditions, which PR-ENDO overcomes by incorporating a specialized diffuse MLP that utilizes light angles and normal vectors, achieving stable reconstructions even with limited training camera rotations. We benchmarked our framework using a publicly available dataset and a newly introduced dataset with wider camera rotations. Our methods demonstrated superior image quality compared to baseline approaches.
Related papers
- IXGS-Intraoperative 3D Reconstruction from Sparse, Arbitrarily Posed Real X-rays [1.2721397985664153]
We extend the $R2$-Gaussian splatting framework to reconstruct consistent 3D volumes under challenging conditions.
We introduce an anatomy-guided radiographic standardization step using style transfer, improving visual consistency across views.
arXiv Detail & Related papers (2025-04-20T18:28:13Z) - X-Field: A Physically Grounded Representation for 3D X-ray Reconstruction [25.13707706037451]
X-ray imaging is indispensable in medical diagnostics, yet its use is tightly regulated due to potential health risks.<n>Recent research focuses on generating novel views from sparse inputs and reconstructing Computed Tomography (CT) volumes.<n>We introduce X-Field, the first 3D representation specifically designed for X-ray imaging.
arXiv Detail & Related papers (2025-03-11T16:31:56Z) - EndoPBR: Material and Lighting Estimation for Photorealistic Surgical Simulations via Physically-based Rendering [1.03590082373586]
The lack of labeled datasets in 3D vision for surgical scenes inhibits the development of robust 3D reconstruction algorithms.<n>We introduce a differentiable rendering framework for material and lighting estimation from endoscopic images and known geometry.<n>By grounding color predictions in the rendering equation, we can generate photorealistic images at arbitrary camera poses.
arXiv Detail & Related papers (2025-02-28T02:50:59Z) - 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) - RelitLRM: Generative Relightable Radiance for Large Reconstruction Models [52.672706620003765]
We propose RelitLRM for generating high-quality Gaussian splatting representations of 3D objects under novel illuminations.
Unlike prior inverse rendering methods requiring dense captures and slow optimization, RelitLRM adopts a feed-forward transformer-based model.
We show our sparse-view feed-forward RelitLRM offers competitive relighting results to state-of-the-art dense-view optimization-based baselines.
arXiv Detail & Related papers (2024-10-08T17:40:01Z) - Deep intra-operative illumination calibration of hyperspectral cameras [73.08443963791343]
Hyperspectral imaging (HSI) is emerging as a promising novel imaging modality with various potential surgical applications.
We show that dynamically changing lighting conditions in the operating room dramatically affect the performance of HSI applications.
We propose a novel learning-based approach to automatically recalibrating hyperspectral images during surgery.
arXiv Detail & Related papers (2024-09-11T08:30:03Z) - EndoSparse: Real-Time Sparse View Synthesis of Endoscopic Scenes using Gaussian Splatting [39.60431471170721]
3D reconstruction of biological tissues from a collection of endoscopic images is a key to unlock various important downstream surgical applications with 3D capabilities.
Existing methods employ various advanced neural rendering techniques for view synthesis, but they often struggle to recover accurate 3D representations when only sparse observations are available.
We propose a framework leveraging the prior knowledge from multiple foundation models during the reconstruction process, dubbed as textitEndoSparse.
arXiv Detail & Related papers (2024-07-01T07:24:09Z) - EndoGSLAM: Real-Time Dense Reconstruction and Tracking in Endoscopic Surgeries using Gaussian Splatting [53.38166294158047]
EndoGSLAM is an efficient approach for endoscopic surgeries, which integrates streamlined representation and differentiable Gaussianization.
Experiments show that EndoGSLAM achieves a better trade-off between intraoperative availability and reconstruction quality than traditional or neural SLAM approaches.
arXiv Detail & Related papers (2024-03-22T11:27:43Z) - FLex: Joint Pose and Dynamic Radiance Fields Optimization for Stereo Endoscopic Videos [79.50191812646125]
Reconstruction of endoscopic scenes is an important asset for various medical applications, from post-surgery analysis to educational training.
We adress the challenging setup of a moving endoscope within a highly dynamic environment of deforming tissue.
We propose an implicit scene separation into multiple overlapping 4D neural radiance fields (NeRFs) and a progressive optimization scheme jointly optimizing for reconstruction and camera poses from scratch.
This improves the ease-of-use and allows to scale reconstruction capabilities in time to process surgical videos of 5,000 frames and more; an improvement of more than ten times compared to the state of the art while being agnostic to external tracking information
arXiv Detail & Related papers (2024-03-18T19:13:02Z) - Domain adaptation strategies for 3D reconstruction of the lumbar spine using real fluoroscopy data [9.21828361691977]
This study tackles key obstacles in adopting surgical navigation in orthopedic surgeries.
It shows an approach for generating 3D anatomical models of the spine from only a few fluoroscopic images.
It achieved an 84% F1 score, matching the accuracy of our previous synthetic data-based research.
arXiv Detail & Related papers (2024-01-29T10:22:45Z) - UniDream: Unifying Diffusion Priors for Relightable Text-to-3D Generation [101.2317840114147]
We present UniDream, a text-to-3D generation framework by incorporating unified diffusion priors.
Our approach consists of three main components: (1) a dual-phase training process to get albedo-normal aligned multi-view diffusion and reconstruction models, (2) a progressive generation procedure for geometry and albedo-textures based on Score Distillation Sample (SDS) using the trained reconstruction and diffusion models, and (3) an innovative application of SDS for finalizing PBR generation while keeping a fixed albedo based on Stable Diffusion model.
arXiv Detail & Related papers (2023-12-14T09:07:37Z) - A Quantitative Evaluation of Dense 3D Reconstruction of Sinus Anatomy
from Monocular Endoscopic Video [8.32570164101507]
We perform a quantitative analysis of a self-supervised approach for sinus reconstruction using endoscopic sequences and optical tracking.
Our results show that the generated reconstructions are in high agreement with the anatomy, yielding an average point-to-mesh error of 0.91 mm.
We identify that pose and depth estimation inaccuracies contribute equally to this error and that locally consistent sequences with shorter trajectories generate more accurate reconstructions.
arXiv Detail & Related papers (2023-10-22T17:11:40Z) - TensoIR: Tensorial Inverse Rendering [51.57268311847087]
TensoIR is a novel inverse rendering approach based on tensor factorization and neural fields.
TensoRF is a state-of-the-art approach for radiance field modeling.
arXiv Detail & Related papers (2023-04-24T21:39:13Z) - OADAT: Experimental and Synthetic Clinical Optoacoustic Data for
Standardized Image Processing [62.993663757843464]
Optoacoustic (OA) imaging is based on excitation of biological tissues with nanosecond-duration laser pulses followed by detection of ultrasound waves generated via light-absorption-mediated thermoelastic expansion.
OA imaging features a powerful combination between rich optical contrast and high resolution in deep tissues.
No standardized datasets generated with different types of experimental set-up and associated processing methods are available to facilitate advances in broader applications of OA in clinical settings.
arXiv Detail & Related papers (2022-06-17T08:11:26Z) - Neural 3D Reconstruction in the Wild [86.6264706256377]
We introduce a new method that enables efficient and accurate surface reconstruction from Internet photo collections.
We present a new benchmark and protocol for evaluating reconstruction performance on such in-the-wild scenes.
arXiv Detail & Related papers (2022-05-25T17:59:53Z) - Tracking monocular camera pose and deformation for SLAM inside the human
body [2.094821665776961]
We propose a novel method to simultaneously track the camera pose and the 3D scene deformation.
The method uses an illumination-invariant photometric method to track image features and estimates camera motion and deformation.
Our results in simulated colonoscopies show the method's accuracy and robustness in complex scenes under increasing levels of deformation.
arXiv Detail & Related papers (2022-04-18T13:25:23Z) - A parameter refinement method for Ptychography based on Deep Learning
concepts [55.41644538483948]
coarse parametrisation in propagation distance, position errors and partial coherence frequently menaces the experiment viability.
A modern Deep Learning framework is used to correct autonomously the setup incoherences, thus improving the quality of a ptychography reconstruction.
We tested our system on both synthetic datasets and also on real data acquired at the TwinMic beamline of the Elettra synchrotron facility.
arXiv Detail & Related papers (2021-05-18T10:15:17Z) - 3D Reconstruction and Alignment by Consumer RGB-D Sensors and Fiducial
Planar Markers for Patient Positioning in Radiation Therapy [1.7744342894757368]
This paper proposes a fast and cheap patient positioning method based on inexpensive consumer level RGB-D sensors.
The proposed method relies on a 3D reconstruction approach that fuses, in real-time, artificial and natural visual landmarks recorded from a hand-held RGB-D sensor.
arXiv Detail & Related papers (2021-03-22T20:20:59Z) - Tattoo tomography: Freehand 3D photoacoustic image reconstruction with
an optical pattern [49.240017254888336]
Photoacoustic tomography (PAT) is a novel imaging technique that can resolve both morphological and functional tissue properties.
A current drawback is the limited field-of-view provided by the conventionally applied 2D probes.
We present a novel approach to 3D reconstruction of PAT data that does not require an external tracking system.
arXiv Detail & Related papers (2020-11-10T09:27:56Z)
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.