We present CT-Bound, a fast boundary estimation method for noisy images using
a hybrid Convolution and Transformer neural network. The proposed architecture
decomposes boundary estimation into two tasks: local detection and global
regularization of image boundaries. It first estimates a parametric
representation of boundary structures only using the input image within a small
receptive field and then refines the boundary structure in the parameter domain
without accessing the input image. Because of this, a part of the network can
be easily trained using naive, synthetic images and still generalized to real
images, and the entire architecture is computationally efficient as the
boundary refinement is non-iterative and not in the image domain. Compared with
the previous highest accuracy methods, our experiment shows that CT-Bound is
100 times faster, producing comparably accurate, high-quality boundary and
color maps. We also demonstrate that CT-Bound can produce boundary and color
maps on real captured images without extra fine-tuning and real-time boundary
map and color map videos at ten frames per second.

通过利用混合的卷积和 Transformer 神经网络，我们提出了一个快速边界估计方法 CT-Bound，用于处理噪声图像。该方法首先利用较小的感受野仅根据输入图像估计边界结构的参数表示，然后在参数领域对边界结构进行优化。相比之前最准确的方法，我们的实验表明，CT-Bound 的速度提高了 100 倍，产生了具有可比较准确性和高质量的边界和颜色地图。同时，我们还展示了 CT-Bound 能够在不需要额外微调的情况下，处理实际捕获的图像，并以每秒十帧的速度生成实时边界地图和颜色地图视频。

CT-Bound: 基于混合卷积和 Transformer 神经网络的噪声图像边界快速估计

CT-Bound: Fast Boundary Estimation From Noisy Images Via Hybrid  Convolution and Transformer Neural Networks

Boundary estimation in images and videos has been a very active topic of
research, and organizing visual information into boundaries and segments is
believed to be a corner stone of visual perception. While prior work has
focused on estimating boundaries for observed frames, our work aims at
predicting boundaries of future unobserved frames. This requires our model to
learn about the fate of boundaries and corresponding motion patterns --
including a notion of "intuitive physics". We experiment on natural video
sequences along with synthetic sequences with deterministic physics-based and
agent-based motions. While not being our primary goal, we also show that fusion
of RGB and boundary prediction leads to improved RGB predictions.

研究了边界估计和预测未来未观察到帧的边界能力，结合直观物理的概念，通过自然视频帧和合成序列的实验，发现 rgb 融合和边缘预测能够促进 rgb 预测的提升。