Monocular depth estimation has received more and more attention due to its wide range of
application scenarios. In this paper, we propose a novel simple framework, called CATNet …

Visual obstacle discovery is a key step towards autonomous navigation of indoor mobile
robots. Successful solutions have many applications in multiple scenes. One of the …

Monocular road segmentation is a fundamental component of autonomous driving. High
accuracy has always been a focus of the community for this task. However, the exploration of …

Raw depth maps captured by depth sensors generally contain missing contents due to
glossy, transparent, and sparsity problems. Recent methods well completed flat regions of …

Text tracking is to track multiple texts in a video, and construct a trajectory for each text.
Existing methods tackle this task by utilizing the tracking-by-detection framework, ie …

Depth and surface normal estimations are important for 3D geometric perception, which has
numerous applications including autonomous vehicles and robots. In this paper, we propose …

Diffusion models have recently shown significant advancement in the generative models
with their impressive fidelity and diversity. The success of these models can be often …

Supervised learning depth estimation methods can achieve good performance when trained
on high-quality ground-truth, like LiDAR data. However, LiDAR can only generate sparse 3D …

The performance of monocular depth estimation generally depends on the amount of
parameters and computational cost. It leads to a large accuracy contrast between light …

To improve the prediction accuracy of monocular depth estimation networks and address
issues such as edge blurring and excessive artifacts in the generated depth maps, this …