Search Results for author:
Found 3 papers, 0 papers with code
StairNetV3: Depth-aware Stair Modeling using Deep Learning
Experiments on our dataset show that our method has a significant improvement over the previous best monocular vision method, with an intersection over union (IOU) increase of 3. 4 %, and the lightweight version has a fast detection speed and can meet the requirements of most real-time applications.
RGB-D-based Stair Detection using Deep Learning for Autonomous Stair Climbing
Specifically, we design a selective module, which can make the network learn the complementary relationship between the RGB map and the depth map and effectively combine the information from the RGB map and the depth map in different scenes.
Deep Leaning-Based Ultra-Fast Stair Detection
Staircases are some of the most common building structures in urban environments.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
