3D colon reconstruction from Optical Colonoscopy (OC) to detect non-examined
surfaces remains an unsolved problem. The challenges arise from the nature of
optical colonoscopy data, characterized by highly reflective low-texture
surfaces, drastic illumination changes and frequent tracking loss. Recent
methods demonstrate compelling results, but suffer from: (1) frangible
frame-to-frame (or frame-to-model) pose estimation resulting in many tracking
failures; or (2) rely on point-based representations at the cost of scan
quality. In this paper, we propose a novel reconstruction framework that
addresses these issues end to end, which result in both quantitatively and
qualitatively accurate and robust 3D colon reconstruction. Our SLAM approach,
which employs correspondences based on contrastive deep features, and deep
consistent depth maps, estimates globally optimized poses, is able to recover
from frequent tracking failures, and estimates a global consistent 3D model;
all within a single framework. We perform an extensive experimental evaluation
on multiple synthetic and real colonoscopy videos, showing high-quality results
and comparisons against relevant baselines.

本文提出了一种新颖的重建框架，采用对比深度特征和深度一致性深度图的对应方法，通过单一框架实现全局优化姿势，能够从频繁的跟踪失败中恢复，并估计全局一致的三维模型，从而实现了量化和质量上准确而健壮的三维结肠重建。

C$^3$Fusion：一致性对比结肠融合，迈向深度结肠镜 SLAM

C$^3$Fusion: Consistent Contrastive Colon Fusion, Towards Deep SLAM in Colonoscopy

Object tracking is challenging as target objects often undergo drastic
appearance changes over time. Recently, adaptive correlation filters have been
successfully applied to object tracking. However, tracking algorithms relying
on highly adaptive correlation filters are prone to drift due to noisy updates.
Moreover, as these algorithms do not maintain long-term memory of target
appearance, they cannot recover from tracking failures caused by heavy
occlusion or target disappearance in the camera view. In this paper, we propose
to learn multiple adaptive correlation filters with both long-term and
short-term memory of target appearance for robust object tracking. First, we
learn a kernelized correlation filter with an aggressive learning rate for
locating target objects precisely. We take into account the appropriate size of
surrounding context and the feature representations. Second, we learn a
correlation filter over a feature pyramid centered at the estimated target
position for predicting scale changes. Third, we learn a complementary
correlation filter with a conservative learning rate to maintain long-term
memory of target appearance. We use the output responses of this long-term
filter to determine if tracking failure occurs. In the case of tracking
failures, we apply an incrementally learned detector to recover the target
position in a sliding window fashion. Extensive experimental results on
large-scale benchmark datasets demonstrate that the proposed algorithm performs
favorably against the state-of-the-art methods in terms of efficiency,
accuracy, and robustness.

提出了一种学习多个自适应关联滤波器的方法，同时具有目标外观的长期和短期记忆，以实现强大的物体跟踪，在大规模基准数据集上表现优异，尤其在效率、准确性和鲁棒性方面均优于现有的技术方法。