Telecystoscopy can lower the barrier to access critical urologic diagnostics for patients around the world. A major challenge for robotic control of flexible cystoscopes and intuitive teleoperation is the pose estimation of the scope tip. We propose a novel real-time camera localization method using video recordings from a prior cystoscopy and 3D bladder reconstruction to estimate cystoscope pose within the bladder during follow-up telecystoscopy. We map prior video frames into a low-dimensional space as a dictionary so that a new image can be likewise mapped to efficiently retrieve its nearest neighbor among the dictionary images. The cystoscope pose is then estimated by the correspondence among the new image, its nearest dictionary image, and the prior model from 3D reconstruction. We demonstrate performance of our methods using bladder phantoms with varying fidelity and a servo-controlled cystoscope to simulate the use case of bladder surveillance through telecystoscopy. The servo-controlled cystoscope with 3 degrees of freedom (angulation, roll, and insertion axes) was developed for collecting cystoscope videos from bladder phantoms. Cystoscope videos were acquired in a 2.5D bladder phantom (bladder-shape cross-section plus height) with a panorama of a urothelium attached to the inner surface. Scans of the 2.5D phantom were performed in separate arc trajectories each of which is generated by actuation on the angulation with a fixed roll and insertion length. We further included variance in moving speed, imaging distance and existence of bladder tumors. Cystoscope videos were also acquired in a water-filled 3D silicone bladder phantom with hand-painted vasculature. Scans of the 3D phantom were performed in separate circle trajectories each of which is generated by actuation on the roll axis under a fixed angulation and insertion length. These videos were used to create 3D reconstructions, dictionary sets, and test data sets for evaluating the computational efficiency and accuracy of our proposed method in comparison with a method based on global Scale-Invariant Feature Transform (SIFT) features, named SIFT-only. Our method can retrieve the nearest dictionary image for 94–100% of test frames in under 55[Formula: see text]ms per image, whereas the SIFT-only method can only find the image match for 56–100% of test frames in 6000–40000[Formula: see text]ms per image depending on size of the dictionary set and richness of SIFT features in the images. Our method, with a speed of around 20 Hz for the retrieval stage, is a promising tool for real-time image-based scope localization in robotic cystoscopy when prior cystoscopy images are available.
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This content will become publicly available on October 1, 2024
Autonomous Aesthetic Views Finding in Indoor Scenes with Deep Reinforcement Learning
Placing and orienting a camera to compose aesthetically
meaningful shots of a scene is not only a key objective in
real-world photography and cinematography but also for
virtual content creation. The framing of a camera often significantly
contributes to the story telling in movies, games,
and mixed reality applications. Generating single camera
poses or even contiguous trajectories either requires a significant
amount of manual labor or requires solving highdimensional
optimization problems, which can be computationally
demanding and error-prone. In this paper, we introduce
GAIT, a Deep Reinforcement Learning (DRL) agent,
that learns to automatically control a camera to generate a
sequence of aesthetically meaningful views for synthetic 3D
indoor scenes. To generate sequences of frames with high
aesthetic value, GAIT relies on a neural aesthetics estimator,
which is trained on a crowed-sourced dataset. Additionally,
we introduce regularization techniques for diversity
and smoothness to generate visually interesting trajectories
for a 3D environment, and to constrain agent acceleration
in the reward function to generate a smooth sequence
of camera frames. We validated our method by comparing
it to baseline algorithms, based on a perceptual user
study, and through ablation studies. The source code of our
method will be released with the final version of our paper.
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- Award ID(s):
- 2107224
- NSF-PAR ID:
- 10435201
- Date Published:
- Journal Name:
- International Conference on Computer Vision
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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