Recently, large models (Segment Anything model) came on the scene to provide
a new baseline for polyp segmentation tasks. This demonstrates that large
models with a sufficient image level prior can achieve promising performance on
a given task. In this paper, we unfold a new perspective on polyp segmentation
modeling by leveraging the Depth Anything Model (DAM) to provide depth prior to
polyp segmentation models. Specifically, the input polyp image is first passed
through a frozen DAM to generate a depth map. The depth map and the input polyp
images are then concatenated and fed into a convolutional neural network with
multiscale to generate segmented images. Extensive experimental results
demonstrate the effectiveness of our method, and in addition, we observe that
our method still performs well on images of polyps with noise. The URL of our
code is https://github.com/zzr-idam/Polyp-DAM.

本文通过利用深度先验的方式，使用大规模模型和卷积神经网络对息肉图像进行分割，在实验证明了该方法的有效性，并观察到该方法在含有噪声的息肉图像上仍表现良好。

Polyp-DAM: 基于深度任意模型的息肉分割

Polyp-DAM: Polyp segmentation via depth anything model

Panoramic image enables deeper understanding and more holistic perception of
$360^\circ$ surrounding environment, which can naturally encode enriched scene
context information compared to standard perspective image. Previous work has
made lots of effort to solve the scene understanding task in a bottom-up form,
thus each sub-task is processed separately and few correlations are explored in
this procedure. In this paper, we propose a novel method using depth prior for
holistic indoor scene understanding which recovers the objects' shapes,
oriented bounding boxes and the 3D room layout simultaneously from a single
panorama. In order to fully utilize the rich context information, we design a
transformer-based context module to predict the representation and relationship
among each component of the scene. In addition, we introduce a real-world
dataset for scene understanding, including photo-realistic panoramas,
high-fidelity depth images, accurately annotated room layouts, and oriented
object bounding boxes and shapes. Experiments on the synthetic and real-world
datasets demonstrate that our method outperforms previous panoramic scene
understanding methods in terms of both layout estimation and 3D object
detection.

本文提出了一种使用深度先验的方法，从单个全景图中同时复原物体形状、定向边界框和三维房间布局，为了充分利用丰富的上下文信息，设计了一个基于 transformer 的上下文模块来预测场景各组成部分之间的表示和关系。实验表明，我们的方法在布局估计和三维物体检测方面优于先前的全景场景理解方法。同时，本文还引入了一个包括照片逼真的全景图、高保真深度图、精确注释的房间布局和定向物体边界框和形状的真实世界数据集。