Designing deep networks for surface normal estimation

Citations Over TimeTop 1% of 2015 papers

Abstract

In the past few years, convolutional neural nets (CNN) have shown incredible promise for learning visual representations. In this paper, we use CNNs for the task of predicting surface normals from a single image. But what is the right architecture? We propose to build upon the decades of hard work in 3D scene understanding to design a new CNN architecture for the task of surface normal estimation. We show that incorporating several constraints (man-made, Manhattan world) and meaningful intermediate representations (room layout, edge labels) in the architecture leads to state of the art performance on surface normal estimation. We also show that our network is quite robust and show state of the art results on other datasets as well without any fine-tuning.

Related Papers

• → An Adaptable Real-Time Object Detection for Traffic Surveillance using R-CNN over CNN with Improved Accuracy(2022)12 cited
• → Implementing convolutional neural network model for prediction in medical imaging(2022)6 cited
• → Deep fake Detection Through Deep Learning(2023)4 cited
• Why & When Deep Learning Works: Looking Inside Deep Learnings.(2017)
• → Convolutional Neural Network (CNN) Applied to the Risk Analysis of Accidents in Vessels Navigating the Amazon Rivers(2023)