Visual odometry plays a crucial role in endoscopic imaging, yet the scarcity
of realistic images with ground truth poses poses a significant challenge.
Therefore, domain adaptation offers a promising approach to bridge the
pre-operative planning domain with the intra-operative real domain for learning
odometry information. However, existing methodologies suffer from
inefficiencies in the training time. In this work, an efficient neural style
transfer framework for endoscopic visual odometry is proposed, which compresses
the time from pre-operative planning to testing phase to less than five
minutes. For efficient traing, this work focuses on training modules with only
a limited number of real images and we exploit pre-operative prior information
to dramatically reduce training duration. Moreover, during the testing phase,
we propose a novel Test Time Adaptation (TTA) method to mitigate the gap in
lighting conditions between training and testing datasets. Experimental
evaluations conducted on two public endoscope datasets showcase that our method
achieves state-of-the-art accuracy in visual odometry tasks while boasting the
fastest training speeds. These results demonstrate significant promise for
intra-operative surgery applications.

提出了一种用于内窥镜视觉测距的高效神经风格转换框架，将术前规划领域与术中真实领域进行培训信息的域适应，以在不到五分钟的时间内压缩从术前规划到测试阶段的时间，并通过利用术前先验信息在训练模块中仅使用有限数量的真实图像来大大缩短训练时间。在测试阶段，提出了一种新颖的测试时间适应方法来减轻训练和测试数据集之间光照条件的差距。在两个公共内窥镜数据集上进行的实验评估显示，该方法在视觉测距任务中实现了最先进的准确性，同时拥有最快的训练速度，这些结果对术中手术应用具有重要的前景。

内窥镜视觉里程计的高效领域自适应

Efficient Domain Adaptation for Endoscopic Visual Odometry

Data augmentation has been actively studied for robust neural networks. Most
of the recent data augmentation methods focus on augmenting datasets during the
training phase. At the testing phase, simple transformations are still widely
used for test-time augmentation. This paper proposes a novel instance-level
test-time augmentation that efficiently selects suitable transformations for a
test input. Our proposed method involves an auxiliary module to predict the
loss of each possible transformation given the input. Then, the transformations
having lower predicted losses are applied to the input. The network obtains the
results by averaging the prediction results of augmented inputs. Experimental
results on several image classification benchmarks show that the proposed
instance-aware test-time augmentation improves the model's robustness against
various corruptions.

该研究提出了一种基于实例的测试时间数据增强方法，使用辅助模块来预测每种可能的转换的损失，并将预测损失较低的转换应用于输入，实验结果表明该方法提高了模型对多种损坏的鲁棒性。