Instruct: Given the title of a paper, retrieve paper titles that are cited by the given paper.
Query: Joint prediction of depths, normals and surface curvature from RGB images using CNNs
Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-scale Convolutional Architecture
Deep attention-based classification network for robust depth prediction
SurfNet: Generating 3D Shape Surfaces Using Deep Residual Networks
Curriculum Learning for Depth Estimation with Deep Convolutional Neural Networks
Monocular Depth Prediction Using Generative Adversarial Networks
DDRNet: Depth Map Denoising and Refinement for Consumer Depth Cameras Using Cascaded CNNs
Curve-Structure Segmentation from Depth Maps: A CNN-based Approach and Its Application to Exploring Cultural Heritage Objects
ENG: End-to-end Neural Geometry for Robust Depth and Pose Estimation using CNNs
Color-Guided Depth Map Super Resolution Using Convolutional Neural Network
Segmentation and semantic labelling of RGBD data with convolutional neural networks and surface fitting
Night time road curvature estimation based on Convolutional Neural Networks
Deep Learning 3D Shape Surfaces Using Geometry Images
Deep convolutional neural fields for depth estimation from a single image
Variational Regularization and Fusion of Surface Normal Maps
Analyzing Modular CNN Architectures for Joint Depth Prediction and Semantic Segmentation
Monocular Depth Estimation Using Neural Regression Forest
Convolutional Neural Networks on 3D Surfaces Using Parallel Frames
Towards unified depth and semantic prediction from a single image
Fully Convolutional Network for Depth Estimation and Semantic Segmentation
Joint Semantic Segmentation and Depth Estimation with Deep Convolutional Networks
Customized Organization of Social Media Contents using Focused Topic Hierarchy
Contrasting and Combining Least Squares Based Learners for Emotion Recognition in the Wild
Understanding Deep Features with Computer-Generated Imagery
Augmented Reality, Virtual Learning Environment and Mobile Learning in education: A comparison
150 years of Darwin’s theory of intercellular flow of hereditary information