000112261 001__ 112261 000112261 005__ 20220510091958.0 000112261 037__ $$aTAZ-TFM-2021-1495 000112261 041__ $$aeng 000112261 1001_ $$aMartínez Batlle, Víctor 000112261 24200 $$aReal-scale 3D reconstruction from monocular endoscope images. 000112261 24500 $$aReconstrucción 3D a escala real a partir de imágenes monoculares de endoscopio. 000112261 260__ $$aZaragoza$$bUniversidad de Zaragoza$$c2021 000112261 506__ $$aby-nc-sa$$bCreative Commons$$c3.0$$uhttp://creativecommons.org/licenses/by-nc-sa/3.0/ 000112261 520__ $$aIn today's scientific community, there is a growing research interest in extending augmented reality and autonomous navigation into the human body. The European EndoMapper project aims to solve the problem of real-time 3D mapping of the human colon that will assist colonoscopy, tumour biopsy and other medical procedures. Due to space limitations inside the human bowel, medical endoscopy can only use monocular vision. However, conventional mapping systems are not able to recover the real scale of the environment from monocular images. This intrinsic limitation leads to unknown scale maps, making it difficult to diagnose some diseases of the human colon. In addition, these maps are often distorted, as they suffer from scale drift, which is a common problem in monocular systems.This MEng thesis proposes a solution to monocular real-scale 3D reconstruction inside the human colon. Our approach exploits the controlled lighting inside the human body, where the only light source moves jointly with the camera. This allows us to consider a pseudo-stereo pair formed by the endoscope's light and camera, and use it to achieve real-scale perception on a monocular endoscope. First, we define a model of the illumination and camera of an endoscope, which allows us to understand the imaging process during colonoscopy. Then, this model is adapted and calibrated for a real medical endoscope, using only a sequence of images recorded by the endoscope itself. Finally, we propose a method capable of estimating a dense depth map from a single monocular image, based on the calibration we performed. To evaluate the accuracy of our depth estimation, we conducted experiments with both synthetic and real images of the human colon. With respect to synthetic data, we obtain results with a 7% error, which is less than 3 mm on average. Lastly, we demonstrate that our method can also work with real images, where we estimate dense depth maps that preserve the structure and discontinuities of the human colon.<br /> 000112261 521__ $$aMáster Universitario en Robótica, Gráficos y Visión por Computador 000112261 540__ $$aDerechos regulados por licencia Creative Commons 000112261 700__ $$aTardós Solano, Juan Domingo$$edir. 000112261 7102_ $$aUniversidad de Zaragoza$$bInformática e Ingeniería de Sistemas$$cIngeniería de Sistemas y Automática 000112261 8560_ $$f736478@unizar.es 000112261 8564_ $$s3943482$$uhttps://zaguan.unizar.es/record/112261/files/TAZ-TFM-2021-1495.pdf$$yMemoria (eng) 000112261 909CO $$ooai:zaguan.unizar.es:112261$$pdriver$$ptrabajos-fin-master 000112261 950__ $$a 000112261 951__ $$adeposita:2022-05-10 000112261 980__ $$aTAZ$$bTFM$$cEINA 000112261 999__ $$a20211125215503.CREATION_DATE