/RGBD-semantic-segmentation

A paper list of RGBD semantic segmentation (processing)

RGBD semantic segmentation

A paper list of RGBD semantic segmentation.

*Last updated: 2020/11/18

Update log

2020/May - update all of recent papers and make some diagram about history of RGBD semantic segmentation.
2020/July - update some recent papers (CVPR2020) of RGBD semantic segmentation.
2020/August - update some recent papers (ECCV2020) of RGBD semantic segmentation.
2020/October - update some recent papers (CVPR2020, WACV2020) of RGBD semantic segmentation.
2020/November - update some recent papers (ECCV2020, arXiv), the links of papers and codes for RGBD semantic segmentation.

Table of Contents

Datasets

The papers related to datasets used mainly in natural/color image segmentation are as follows.

  • [NYUDv2] The NYU-Depth V2 dataset consists of 1449 RGB-D images showing interior scenes, which all labels are usually mapped to 40 classes. The standard training and test set contain 795 and 654 images, respectively.
  • [SUN RGB-D] The SUN RGB-D dataset contains 10,335 RGBD images with semantic labels organized in 37 categories. The 5,285 images are used for training, and 5050 images are used for testing.
  • [2D-3D-S] Stanford-2D-3D-Semantic dataset contains 70496 RGB and depth images as well as 2D annotation with 13 object categories. Areas 1, 2, 3, 4, and 6 are utilized as the training and Area 5 is used as the testing set.

Metrics

The papers related to metrics used mainly in RGBD semantic segmentation are as follows.

  • [PixAcc] Pixel accuracy
  • [mAcc] Mean accuracy
  • [mIoU] Mean intersection over union
  • [f.w.IOU] Frequency weighted IOU

Performance tables

Speed is related to the hardware spec (e.g. CPU, GPU, RAM, etc), so it is hard to make an equal comparison. We select four indexes namely PixAcc, mAcc, mIoU, and f.w.IOU to make comparison. The closer the segmentation result is to the ground truth, the higher the above four indexes are.

NYUDv2

Method PixAcc mAcc mIoU f.w.IOU Input Ref. from Published Year
POR 59.1 28.4 29.1 RGBD CVPR 2013
RGBD R-CNN 60.3 35.1 31.3 47(in LSD-GF) RGBD ECCV 2014
DeconvNet 69.9 56.4 42.7 56 RGB LSD-GF ICCV 2015
DeepLab 68.7 46.9 36.8 52.5 RGBD STD2P ICLR 2015
CRF-RNN 66.3 48.9 35.4 51 RGBD STD2P ICCV 2015
Multi-Scale CNN 65.6 45.1 34.1 51.4 RGB LCSF-Deconv ICCV 2015
FCN 65.4 46.1 34 49.5 RGBD LCSF-Deconv CVPR 2015
Mutex Constraints 63.8 31.5 48.5 (in LSD-GF) RGBD ICCV 2015
E2S2 58.1 52.9 31 44.2 RGBD STD2P ECCV 2016
BI-3000 58.9 39.3 27.7 43 RGBD STD2P ECCV 2016
BI-1000 57.7 37.8 27.1 41.9 RGBD STD2P ECCV 2016
LCSF-Deconv 47.3 RGBD ECCV 2016
LSTM-CF 49.4 RGBD ECCV 2016
RDFNet-152 76 62.8 50.1 RGBD ICCV 2017
SCN-ResNet152 49.6 RGBD ICCV 2017
RDFNet-50 74.8 60.4 47.7 RGBD ICCV 2017
CFN(RefineNet) 47.7 RGBD ICCV 2017
RefineNet-152 73.6 58.9 46.5 RGB CVPR 2017
LSD-GF 71.9 60.7 45.9 59.3 RGBD CVPR 2017
3D-GNN 55.7 43.1 RGBD ICCV 2017
DML-Res50 40.2 RGB IJCAI 2017
STD2P 70.1 53.8 40.1 55.7 RGBD CVPR 2017
PBR-CNN 33.2 RGB ICCBS 2017
B-SegNet 68 45.8 32.4 RGB BMVC 2017
FC-CRF 63.1 39 29.5 48.4 RGBD TIP 2017
LCR 55.6 31.7 21.8 39.9 RGBD ICIP 2017
SegNet 54.1 30.5 21 38.5 RGBD LCR TPAMI 2017
D-Refine-152 74.1 59.5 47 RGB ICPR 2018
TRL-ResNet50 76.2 56.3 46.4 RGB ECCV 2018
D-CNN 56.3 43.9 RGBD ECCV 2018
RGBD-Geo 70.3 51.7 41.2 54.2 RGBD MTA 2018
Context 70 53.6 40.6 RGB TPAMI 2018
DeepLab-LFOV 70.3 49.6 39.4 54.7 RGBD STD2P TPAMI 2018
D-depth-reg 66.7 46.3 34.8 50.6 RGBD PRL 2018
PU-Loop 72.1 44.5 RGB CVPR 2018
C-DCNN 69 50.8 39.8 RGB TNNLS 2018
GAD 84.8 68.7 59.6 RGB CVPR 2019
CTS-IM 76.3 50.6 RGBD ICIP 2019
PAP 76.2 62.5 50.4 RGB CVPR 2019
KIL-ResNet101 75.1 58.4 50.2 RGB ACPR 2019
2.5D-Conv 75.9 49.1 RGBD ICIP 2019
ACNet 48.3 RGBD ICIP 2019
3M2RNet 76 63 48 RGBD SIC 2019
FDNet-16s 73.9 60.3 47.4 RGB AAAI 2019
DMFNet 74.4 59.3 46.8 RGBD IEEE Access 2019
MMAF-Net-152 72.2 59.2 44.8 RGBD arXiv 2019
RTJ-AA 42 RGB ICRA 2019
JTRL-ResNet50 81.3 60.0 50.3 RGB TPAMI 2019
SGNet 76.8 63.1 51 RGBD arXiv 2020
SCN-ResNet101 48.3 RGBD TCYB 2020
RefineNet-Res152-Pool4 74.4 59.6 47.6 RGB TPAMI 2020
TSNet 73.5 59.6 46.1 RGBD IEEE IS 2020
PSD-ResNet50 77.0 58.6 51.0 RGB CVPR 2020
Malleable 2.5D 76.9 50.9 RGBD ECCV 2020
BCMFP+SA-Gate 77.9 52.4 RGBD ECCV 2020
MTI-Net 75.3 62.9 49.0 RGB ECCV 2020
VCD+RedNet 63.5 50.7 RGBD CVPR 2020
VCD+ACNet 64.4 51.9 RGBD CVPR 2020
SANet 75.9 50.7 RGB arXiv 2020
ESANet 50.5 RGBD arXiv 2020

SUN RGB-D

Method PixAcc mAcc mIoU f.w.IOU Input Ref. from Published Year
FCN 68.2 38.4 27.4 RGB SegNet CVPR 2015
DeconvNet 66.1 32.3 22.6 RGB SegNet ICCV 2015
IFCN 77.7 55.5 42.7 RGB arXiv 2016
CFN(RefineNet) 48.1 RGBD ICCV 2017
RDFNet-152 81.5 60.1 47.7 RGBD ICCV 2017
RefineNet-Res152 80.6 58.5 45.9 RGB CVPR 2017
3D-GNN 57 45.9 RGBD ICCV 2017
DML-Res50 42.3 RGB IJCAI 2017
HP-SPS 75.7 50.1 38 RGB BMVC 2017
FuseNet 76.3 48.3 37.3 RGBD ACCV 2017
LRN 72.5 46.8 33.1 RGB arXiv 2017
SegNet 72.6 44.8 31.8 RGB MMAF-Net-152 TPAMI 2017
B-SegNet 71.2 45.9 30.7 RGB BMVC 2017
LSD-GF 58 RGBD CVPR 2017
TRL-ResNet101 84.3 58.9 50.3 RGB ECCV 2018
CCF-GMA 81.4 60.3 47.1 RGB CVPR 2018
D-Refine-152 80.8 58.9 46.3 RGB ICPR 2018
Context 78.4 53.4 42.3 RGB TPAMI 2018
D-CNN 53.5 42 RGBD ECCV 2018
G-FRNet-Res101 75.3 47.5 36.9 RGB arXiv 2018
DeepLab-LFOV 71.9 42.2 32.1 RGB TPAMI 2018
PU-Loop 80.3 45.1 RGB CVPR 2018
C-DCNN 77.3 50 39.4 RGB TNNLS 2018
GAD 85.5 74.9 54.5 RGB CVPR 2019
KIL-ResNet101 84.8 58 52 RGB ACPR 2019
PAP 83.8 58.4 50.5 RGB CVPR 2019
3M2RNet 83.1 63.5 49.8 RGBD SIC 2019
CTS 82.4 48.5 RGBD ICIP 2019
2.5D-Conv 82.4 48.2 RGBD ICIP 2019
ACNet 48.1 RGBD ICIP 2019
MMAF-Net-152 81 58.2 47 RGBD arXiv 2019
LCR-RGBD 42.4 RGBD CVPRW 2019
EFCN-8s 76.9 53.5 40.7 RGB TIP 2019
DSNet 75.6 32.1 RGB ICASSP 2019
JTRL-ResNet101 84.8 59.1 50.8 RGB TPAMI 2019
SCN-ResNet152 50.7 RGBD TCYB 2020
SGNet 81.8 60.9 48.5 RGBD arXiv 2020
CGBNet 82.3 61.3 48.2 RGB TIP 2020
CANet-ResNet101 81.9 47.7 RGB arXiv 2020
RefineNet-Res152-Pool4 81.1 57.7 47 RGB TPAMI 2020
PSD-ResNet50 84.0 57.3 50.6 RGB CVPR 2020
BCMFP+SA-Gate 82.5 49.4 RGBD ECCV 2020
QGN 82.4 45.4 RGBD WACV 2020
VCD+RedNet 62.9 50.3 RGBD CVPR 2020
VCD+ACNet 64.1 51.2 RGBD CVPR 2020
SANet 82.3 51.5 RGB arXiv 2020
ESANet 48.3 RGBD arXiv 2020

2D-3D-S

Method PixAcc mAcc mIoU f.w.IOU Input Ref. from Published Year
Deeplab 64.3 46.7 35.5 48.5 RGBD MMAF-Net-152 ICLR 2015
DeepLab-LFOV 88.0 42.2 69.8 RGB PU-Loop TPAMI 2018
D-CNN 65.4 55.5 39.5 49.9 RGBD ECCV 2018
PU-Loop 91.0 76.5 RGB CVPR 2018
MMAF-Net-152 76.5 62.3 52.9 RGBD arXiv 2019
3M2RNet 79.8 75.2 63 RGBD SIC 2019

Paper list

  • [POR] Gupta, S., et al. (2013). Perceptual Organization and Recognition of Indoor Scenes from RGB-D Images. IEEE Conference on Computer Vision and Pattern Recognition: 564-571. [Paper] [code]
  • [RGBD R-CNN] Gupta, S., et al. (2014). Learning Rich Features from RGB-D Images for Object Detection and Segmentation. European Conference on Computer Vision: 345-360. [Paper] [code]
  • [FCN] Long, J., et al. (2015). Fully convolutional networks for semantic segmentation. IEEE Conference on Computer Vision and Pattern Recognition: 3431-3440. [Paper] [code]
  • [CRF-RNN] Zheng, S., et al. (2015). Conditional Random Fields as Recurrent Neural Networks. IEEE International Conference on Computer Vision: 1529-1537. [Paper] [code]
  • [Mutex Constraints] Deng, Z., et al. (2015). Semantic Segmentation of RGBD Images with Mutex Constraints. IEEE International Conference on Computer Vision: 1733-1741. [Paper] [code]
  • [DeepLab] Chen, L., et al. (2015). Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs. International Conference on Learning Representations. [Paper] [code]
  • [Multi-Scale CNN] Eigen, D. and R. Fergus (2015). Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-scale Convolutional Architecture. IEEE International Conference on Computer Vision: 2650-2658. [Paper] [code]
  • [DeconvNet] Noh, H., et al. (2015). Learning Deconvolution Network for Semantic Segmentation. International Conference on Computer Vision: 1520-1528. [Paper] [code]
  • [LSTM-CF] Li, Z., et al. (2016). LSTM-CF: Unifying Context Modeling and Fusion with LSTMs for RGB-D Scene Labeling. European Conference on Computer Vision: 541-557. [Paper] [code]
  • [LCSF-Deconv] Wang, J., et al. (2016). Learning Common and Specific Features for RGB-D Semantic Segmentation with Deconvolutional Networks. European Conference on Computer Vision: 664-679. [Paper] [code]
  • [BI] Gadde, R., et al. (2016). Superpixel Convolutional Networks using Bilateral Inceptions. European Conference on Computer Vision: 597-613. [Paper] [code]
  • [E2S2] Caesar, H., et al. (2016). Region-Based Semantic Segmentation with End-to-End Training. European Conference on Computer Vision: 381-397. [Paper] [code]
  • [IFCN] Shuai, B., et al. (2016). Improving Fully Convolution Network for Semantic Segmentation. arXiv:1611.08986. [Paper] [code]
  • [SegNet] Badrinarayanan, V., et al. (2017). SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 39(12): 2481-2495. [Paper] [code]
  • [LSD-GF] Cheng, Y., et al. (2017). Locality-Sensitive Deconvolution Networks with Gated Fusion for RGB-D Indoor Semantic Segmentation. IEEE Conference on Computer Vision and Pattern Recognition: 1475-1483. [Paper] [code]
  • [LCR] Chu, J., et al. (2017). Learnable contextual regularization for semantic segmentation of indoor scene images. IEEE International Conference on Image Processing: 1267-1271. [Paper] [code]
  • [RefineNet] Lin, G., et al. (2017). RefineNet: Multi-path Refinement Networks for High-Resolution Semantic Segmentation. IEEE Conference on Computer Vision and Pattern Recognition : 5168-5177, [Paper] [code1] [code2]
  • [FuseNet] Hazirbas, C., et al. (2017). FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-Based CNN Architecture. Asian Conference on Computer Vision: 213-228. [Paper] [code]
  • [STD2P] He, Y., et al. (2017). STD2P: RGBD Semantic Segmentation Using Spatio-Temporal Data-Driven Pooling. IEEE Conference on Computer Vision and Pattern Recognition: 7158-7167. [Paper] [code]
  • [RDFNet] Lee, S., et al. (2017). RDFNet: RGB-D Multi-level Residual Feature Fusion for Indoor Semantic Segmentation. IEEE International Conference on Computer Vision: 4990-4999. [Paper] [code]
  • [CFN(RefineNet)] Lin, D., et al. (2017). Cascaded Feature Network for Semantic Segmentation of RGB-D Images. IEEE International Conference on Computer Vision: 1320-1328. [Paper] [code]
  • [3D-GNN] Qi, X., et al. (2017). 3D Graph Neural Networks for RGBD Semantic Segmentation. IEEE International Conference on Computer Vision: 5209-5218. [Paper] [code1] [code2]
  • [DML-Res50] Shen, T., et al. (2017). Learning Multi-level Region Consistency with Dense Multi-label Networks for Semantic Segmentation. Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence: 2708-2714. [Paper] [code]
  • [PBR-CNN] Zhang, Y., et al. (2017). Physically-Based Rendering for Indoor Scene Understanding Using Convolutional Neural Networks. IEEE Conference on Computer Vision and Pattern Recognition: 5057-5065. [Paper] [code]
  • [FC-CRF] Liu, F., et al. (2017). Discriminative Training of Deep Fully Connected Continuous CRFs With Task-Specific Loss. IEEE Transactions on Image Processing 26(5), 2127-2136. [Paper] [code]
  • [HP-SPS] Park, H., et al. (2017). Superpixel-based semantic segmentation trained by statistical process control. British Machine Vision Conference. [Paper] [code]
  • [LRN] Islam, M. A., et al. (2017). Label Refinement Network for Coarse-to-Fine Semantic Segmentation. arXiv1703.00551. [Paper] [code]
  • [G-FRNet-Res101] Islam, M. A., et al. (2018). Gated Feedback Refinement Network for Coarse-to-Fine Dense Semantic Image Labeling. arXiv:1806.11266 [Paper] [code]
  • [CCF-GMA] Ding, H., et al. (2018). Context Contrasted Feature and Gated Multi-scale Aggregation for Scene Segmentation. IEEE Conference on Computer Vision and Pattern Recognition: 2393-2402. [Paper] [code]
  • [Context] Lin, G., et al. (2018). Exploring Context with Deep Structured Models for Semantic Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(6), 1352-1366. [Paper] [code]
  • [D-Refine-152] Chang, M., et al. (2018). Depth-assisted RefineNet for Indoor Semantic Segmentation. International Conference on Pattern Recognition: 1845-1850. [Paper] [code]
  • [D-depth-reg] Guo, Y. and T. Chen (2018). Semantic segmentation of RGBD images based on deep depth regression. Pattern Recognition Letters 109: 55-64. [Paper] [code]
  • [RGBD-Geo] Liu, H., et al. (2018). RGB-D joint modeling with scene geometric information for indoor semantic segmentation. Multimedia Tools and Applications 77(17): 22475-22488. [Paper] [code]
  • [D-CNN] Wang, W. and U. Neumann (2018). Depth-aware CNN for RGB-D Segmentation. European Conference on Computer Vision: 144-161. [Paper] code
  • [TRL-ResNet50/101] Zhang, Z., et al. (2018). Joint Task-Recursive Learning for Semantic Segmentation and Depth Estimation. European Conference on Computer Vision. [Paper] [code]
  • [DeepLab-LFOV] Chen, L., et al. (2018). DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4), 834-848. [Paper] [code]
  • [PU-Loop] Kong, S. and C. Fowlkes (2018). Recurrent Scene Parsing with Perspective Understanding in the Loop. IEEE Conference on Computer Vision and Pattern Recognition: 956-965. [Paper] [code]
  • [PAD-Net] Xu, D., et al. (2018). PAD-Net: Multi-Tasks Guided Prediction-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing. IEEE Conference on Computer Vision and Pattern Recognition: 675-684. [Paper] [code]
  • [C-DCNN] Liu, J., et al. (2018) Collaborative Deconvolutional Neural Networks for Joint Depth Estimation and Semantic Segmentation. IEEE Transactions on Neural Networks and Learning Systems 29(11): 5655-5666. [Paper] [code]
  • [EFCN-8s] Shuai, B., et al. (2019). Toward Achieving Robust Low-Level and High-Level Scene Parsing. IEEE Transactions on Image Processing, 28(3), 1378-1390. [Paper] [code]
  • [3M2RNet] Fooladgar, F., and Kasaei, S. (2019). 3M2RNet: Multi-Modal Multi-Resolution Refinement Network for Semantic Segmentation. Science and Information Conference: 544-557. [Paper] [code]
  • [RFBNet] Deng, L., et al. (2019). RFBNet: Deep Multimodal Networks with Residual Fusion Blocks for RGB-D Semantic Segmentation. arXiv:1907.00135 [Paper] [code]
  • [MMAF-Net-152] Fooladgar, F. and S. Kasaei (2019). "Multi-Modal Attention-based Fusion Model for Semantic Segmentation of RGB-Depth Images." arXiv:1912.11691. [Paper] [code]
  • [LCR-RGBD] Giannone, G. and B. Chidlovskii (2019). Learning Common Representation from RGB and Depth Images. IEEE Conference on Computer Vision and Pattern Recognition Workshops. [Paper] [code]
  • [ACNet] Hu, X., et al. (2019). ACNET: Attention Based Network to Exploit Complementary Features for RGBD Semantic Segmentation. IEEE International Conference on Image Processing: 1440-1444. [Paper] [code]
  • [DSNet] Jiang, F., et al. (2019). DSNET: Accelerate Indoor Scene Semantic Segmentation. IEEE International Conference on Acoustics, Speech and Signal Processing: 3317-3321. [Paper] [code]
  • [GAD] Jiao, J., et al. (2019). Geometry-Aware Distillation for Indoor Semantic Segmentation. IEEE Conference on Computer Vision and Pattern Recognition: 2864-2873. [Paper] [code]
  • [RTJ-AA] Nekrasov, V., et al. (2019). Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric Annotations. International Conference on Robotics and Automation: 7101-7107. [Paper] [code]
  • [CTS-IM] Xing, Y., et al. (2019). Coupling Two-Stream RGB-D Semantic Segmentation Network by Idempotent Mappings. IEEE International Conference on Image Processing: 1850-1854. [Paper] [code]
  • [2.5D-Conv] Xing, Y. J., et al. (2019). 2.5d Convolution for RGB-D Semantic Segmentation. IEEE International Conference on Image Processing: 1410-1414. [Paper] [code]
  • [DMFNet] Yuan, J., et al. (2019). DMFNet: Deep Multi-Modal Fusion Network for RGB-D Indoor Scene Segmentation. IEEE Access 7: 169350-169358. [Paper] [code]
  • [PAP] Zhang, Z., et al. (2019). Pattern-Affinitive Propagation Across Depth, Surface Normal and Semantic Segmentation. IEEE Conference on Computer Vision and Pattern Recognition: 4101-4110. [Paper] [code]
  • [KIL-ResNet101] Zhou, L., et al. (2019). KIL: Knowledge Interactiveness Learning for Joint Depth Estimation and Semantic Segmentation. Asian Conference on Pattern Recognition: 835-848. [Paper] [code]
  • [FDNet-16s] Zhen, M., et al. (2019). Learning Fully Dense Neural Networks for Image Semantic Segmentation. The Thirty-Third AAAI Conference on Artificial Intelligence: 9283-9290. [Paper] [code]
  • [JTRL-ResNet50/101] Zhang, Z., et al. (2019). Joint Task-Recursive Learning for RGB-D Scene Understanding. IEEE Transactions on Pattern Analysis and Machine Intelligence. [Paper] [code]
  • [SGNet] Chen, L.-Z., et al. (2020). Spatial Information Guided Convolution for Real-Time RGBD Semantic Segmentation. arXiv:2004.04534. [Paper] [code]
  • [SCN-ResNet101] Lin, D., et al. (2020). SCN: Switchable Context Network for Semantic Segmentation of RGB-D Images. IEEE Transactions on Cybernetics 50(3): 1120-1131. [Paper] [code]
  • [RefineNet-Res152-Pool4] Lin, G., et al. (2020). RefineNet: Multi-Path Refinement Networks for Dense Prediction. IEEE Transactions on Pattern Analysis and Machine Intelligence 42(5): 1228-1242. [Paper] [code]
  • [CANet-ResNet101] Tang, Q., et al. (2020). Attention-guided Chained Context Aggregation for Semantic Segmentation. arXiv:2002.12041. [Paper] [code]
  • [CGBNet] Ding, H., et al. (2020). Semantic Segmentation with Context Encoding and Multi-Path Decoding. IEEE Transactions on Image Processing 29: 3520-3533. [Paper] [code]
  • [TSNet] Zhou, W., et al. (2020). TSNet: Three-stream Self-attention Network for RGB-D Indoor Semantic Segmentation. IEEE Intelligent Systems. [Paper] [code]
  • [PSD-ResNet50] Zhou, L., et al. (2020). Pattern-Structure Diffusion for Multi-Task Learning. IEEE Conference on Computer Vision and Pattern Recognition. [Paper] [code]
  • [Malleable 2.5D] Xing, Y., et al. (2020). Malleable 2.5D Convolution: Learning Receptive Fields along the Depth-axis for RGB-D Scene Parsing. European Conference on Computer Vision. [Paper] [code]
  • [BCMFP+SA-Gate] Chen X., et al. (2020). Bi-directional Cross-Modality Feature Propagation with Separation-and-Aggregation Gate for RGB-D Semantic Segmentation. European Conference on Computer Vision. [Paper] [code]
  • [MTI-Net] Vandenhende S., et al. (2020). MTI-Net: Multi-Scale Task Interaction Networks for Multi-Task Learning. European Conference on Computer Vision. [Paper] [code]
  • [QGN] Kashyap C., et al. (2020). Quadtree Generating Networks: Efficient Hierarchical Scene Parsing with Sparse Convolutions. IEEE Winter Conference on Applications of Computer Vision. [Paper] [code]
  • [VCD+RedNet/ACNet] Xiong, Z.-T., et al. (2020). Variational Context-Deformable ConvNets for Indoor Scene Parsing. IEEE Conference on Computer Vision and Pattern Recognition. [Paper] [code]
  • [SANet] Yu, L., et al. (2020). Multi-layer Feature Aggregation for Deep Scene Parsing Models. arXiv:2011.02572. [Paper] [code]
  • [ESANet] Seichter, D., et al. (2020). Efficient RGB-D Semantic Segmentation for Indoor Scene Analysis. arXiv:2011.06961. [Paper] [code]

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