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Incorporating depth into both CNN and CRF for indoor semantic segmentation

https://doi.org/10.1109/icsess.2017.8342970
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-1-4471-4640-7_8openalex-snapshot
A Category-Level 3D Object Dataset: Putting the Kinect to Work
inferredcites →https://doi.org/10.1007/978-3-319-10584-0_23openalex-snapshot
Learning Rich Features from RGB-D Images for Object Detection and Segmentation
inferredcites →https://doi.org/10.1007/978-3-319-54181-5_14openalex-snapshot
FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-Based CNN Architecture
inferredcites →https://doi.org/10.1007/978-3-642-33715-4_54openalex-snapshot
Indoor Segmentation and Support Inference from RGBD Images
inferredcites →https://doi.org/10.1007/s11263-007-0109-1openalex-snapshot
TextonBoost for Image Understanding: Multi-Class Object Recognition and Segmentation by Jointly Modeling Texture, Layout, and Context
inferredcites →https://doi.org/10.1007/s11263-008-0202-0openalex-snapshot
Robust Higher Order Potentials for Enforcing Label Consistency
inferredcites →https://doi.org/10.1007/s11263-015-0816-yopenalex-snapshot
ImageNet Large Scale Visual Recognition Challenge
inferredcites →https://doi.org/10.1109/cvpr.2004.1315232openalex-snapshot
Multiscale conditional random fields for image labeling
inferredcites →https://doi.org/10.1109/cvpr.2007.383204openalex-snapshot
P3 & Beyond: Solving Energies with Higher Order Cliques
inferredcites →https://doi.org/10.1109/cvpr.2015.7298655openalex-snapshot
SUN RGB-D: A RGB-D scene understanding benchmark suite
inferredcites →https://doi.org/10.1109/cvpr.2015.7298965openalex-snapshot
Fully convolutional networks for semantic segmentation
inferredcites →https://doi.org/10.1109/iccv.2013.458openalex-snapshot
SUN3D: A Database of Big Spaces Reconstructed Using SfM and Object Labels
inferredcites →https://doi.org/10.1109/iccv.2015.178openalex-snapshot
Learning Deconvolution Network for Semantic Segmentation
inferredcites →https://doi.org/10.1109/iccv.2015.179openalex-snapshot
Conditional Random Fields as Recurrent Neural Networks
inferredcites →https://doi.org/10.1109/iccv.2015.191openalex-snapshot
BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation
inferredcites →https://doi.org/10.1109/iccv.2015.304openalex-snapshot
Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-scale Convolutional Architecture
inferredcites →https://doi.org/10.1109/iccvw.2011.6130382openalex-snapshot
A category-level 3-D object dataset: Putting the Kinect to work
inferredcites →https://doi.org/10.1109/icra.2014.6907778openalex-snapshot
Learning depth-sensitive conditional random fields for semantic segmentation of RGB-D images
inferredcites →https://doi.org/10.1109/tpami.2016.2644615openalex-snapshot
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
inferredcites →https://doi.org/10.1109/tpami.2017.2699184openalex-snapshot
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
inferredcites →https://doi.org/10.1109/tpami.2017.2708714openalex-snapshot
Exploring Context with Deep Structured Models for Semantic Segmentation
inferredcites →https://doi.org/10.48550/arxiv.1210.5644openalex-snapshot
Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
inferredcites →https://doi.org/10.48550/arxiv.1301.3572openalex-snapshot
Indoor Semantic Segmentation using depth information
inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-snapshot
Very Deep Convolutional Networks for Large-Scale Image Recognition
inferredcites →https://doi.org/10.48550/arxiv.1502.03167openalex-snapshot
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
inferredcites →https://doi.org/10.48550/arxiv.1503.02351openalex-snapshot
Fully Connected Deep Structured Networks
inferredcites →https://doi.org/10.48550/arxiv.1603.04467openalex-snapshot
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
inferredcites →https://doi.org/10.48550/arxiv.1606.02147openalex-snapshot
ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation
inferredcites →https://doi.org/10.57702/o9raffedopenalex-snapshot
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4824679openalex-extracted
Deep Learning Based 3d Segmentation: A Survey
inferred← citeshttps://doi.org/10.1007/978-3-030-69525-5_31openalex-snapshot
RGB-D Co-attention Network for Semantic Segmentation
inferred← citeshttps://doi.org/10.1007/978-3-031-67569-0_35openalex-snapshot
Vision Based Serial Manipulator Kinematics Identification
inferred← citeshttps://doi.org/10.1007/978-981-19-7943-9_16openalex-snapshot
A Semi-supervised Road Segmentation Method for Remote Sensing Image Based on SegFormer
inferred← citeshttps://doi.org/10.1007/s10462-019-09793-6openalex-snapshot
A comprehensive review of conditional random fields: variants, hybrids and applications
inferred← citeshttps://doi.org/10.1007/s11042-019-07882-wopenalex-snapshot
Indoor scene understanding via RGB-D image segmentation employing depth-based CNN and CRFs
inferred← citeshttps://doi.org/10.1007/s12652-022-03829-6openalex-snapshot
Overview of RGBD semantic segmentation based on deep learning
inferred← citeshttps://doi.org/10.1016/j.cviu.2019.102809openalex-snapshot
Distance transform regression for spatially-aware deep semantic segmentation
inferred← citeshttps://doi.org/10.1016/j.displa.2021.102080openalex-snapshot
A brief survey on RGB-D semantic segmentation using deep learning
inferred← citeshttps://doi.org/10.1016/j.displa.2025.103225openalex-snapshot
Review of deep learning-based segmentation methods: Popular approaches, literature gaps, and opportunities
inferred← citeshttps://doi.org/10.1016/j.imavis.2020.104042openalex-snapshot
Deep multimodal fusion for semantic image segmentation: A survey
inferred← citeshttps://doi.org/10.1016/j.isprsjprs.2020.09.019openalex-snapshot
Parsing very high resolution urban scene images by learning deep ConvNets with edge-aware loss
inferred← citeshttps://doi.org/10.1016/j.neucom.2019.02.003openalex-snapshot
Survey on semantic segmentation using deep learning techniques
inferred← citeshttps://doi.org/10.1016/j.patcog.2021.108468openalex-snapshot
CANet: Co-attention network for RGB-D semantic segmentation
inferred← citeshttps://doi.org/10.1109/access.2017.2729943openalex-snapshot
An Improved Faster R-CNN for Same Object Retrieval
inferred← citeshttps://doi.org/10.1109/ccdc55256.2022.10034106openalex-snapshot
The Network of Attention-Aware Multimodal fusion for RGB-D Indoor Semantic Segmentation Method
inferred← citeshttps://doi.org/10.1109/cvpr52688.2022.00257openalex-snapshot
Generating 3D Bio-Printable Patches Using Wound Segmentation and Reconstruction to Treat Diabetic Foot Ulcers
inferred← citeshttps://doi.org/10.1109/cvprw.2019.00054openalex-snapshot
Learning Common Representation From RGB and Depth Images
inferred← citeshttps://doi.org/10.1109/iccvw.2017.85openalex-snapshot
SnapNet-R: Consistent 3D Multi-view Semantic Labeling for Robotics
inferred← citeshttps://doi.org/10.1109/iccvw.2019.00117openalex-snapshot
Small Obstacle Avoidance Based on RGB-D Semantic Segmentation
inferred← citeshttps://doi.org/10.1109/icmew.2018.8551554openalex-snapshot
RGB-D Semantic Segmentation: A Review
inferred← citeshttps://doi.org/10.1109/icra46639.2022.9811702openalex-snapshot
Striking the Right Balance: Recall Loss for Semantic Segmentation
inferred← citeshttps://doi.org/10.1109/tits.2025.3568500openalex-snapshot
AI-Powered LiDAR Point Cloud Understanding and Processing: An Updated Survey
inferred← citeshttps://doi.org/10.3390/electronics12122730openalex-snapshot
Deep-Learning-Based Approaches for Semantic Segmentation of Natural Scene Images: A Review
inferred← citeshttps://doi.org/10.3390/ijgi9050330openalex-snapshot
A Review of Techniques for 3D Reconstruction of Indoor Environments
inferred← citeshttps://doi.org/10.3390/math11194189openalex-snapshot
Enhancing Brain Tumor Segmentation Accuracy through Scalable Federated Learning with Advanced Data Privacy and Security Measures
inferred← citeshttps://doi.org/10.3724/sp.j.1089.2022.19132openalex-snapshot
RGB-D Image Semantic Segmentation Based on Multi-Modal Adaptive Convolution
inferred← citeshttps://doi.org/10.3837/tiis.2020.11.010openalex-snapshot
DA-Res2Net: a novel Densely connected residual Attention network for image semantic segmentation
inferred← citeshttps://doi.org/10.48550/arxiv.1806.01054openalex-snapshot
RedNet: Residual Encoder-Decoder Network for indoor RGB-D Semantic Segmentation
inferred← citeshttps://doi.org/10.48550/arxiv.1906.11428openalex-snapshot
ELKPPNet: An Edge-aware Neural Network with Large Kernel Pyramid Pooling for Learning Discriminative Features in Semantic Segmentation
inferred← citeshttps://doi.org/10.70675/114e131azb18az4b50zb9d8z55d904a50cbcopenalex-snapshot
Analyse sémantique de l'environnement de conduite dans les scénarios urbains
inferred← citeshttps://doi.org/10.70675/a471d7bez2ff1z44feza0b4z4beabc22b5abopenalex-snapshot
Real-time multimodal semantic scene understanding for autonomous UGV navigation
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