Fetching the paper…
Reading the bibliography…
As camera and LiDAR sensors capture complementary information used in autonomous driving, great efforts have been made to develop semantic segmentation algorithms through multi-modality data fusion.
Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite
Geiger, A., Lenz, P., Urtasun, R.: · 2012
Earlier work this paper cites.
Rectifier nonlinearities improve neural network acoustic models
Maas, A.L., Hannun, A.Y., Ng, A.Y., et al.: · 2013
Earlier work this paper cites.
Distilling the knowledge in a neural network
Hinton, G., Vinyals, O., Dean, J.: · 2014
Earlier work this paper cites.
Do deep nets really need to be deep?
Ba, L.J., Caruana, R.: · 2014
Earlier work this paper cites.
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., Darrell, T.: · 2015
Earlier work this paper cites.
Efficient piecewise training of deep structured models for semantic segmentation
Lin, G., Shen, C., Van Den Hengel, A., Reid, I.: · 2016
Earlier work this paper cites.
Cross modal distillation for supervision transfer
Gupta, S., Hoffman, J., Malik, J.: · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2016
Earlier work this paper cites.
Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: · 2017
Earlier work this paper cites.
Rethinking atrous convolution for semantic image segmentation
Chen, L.C., Papandreou, G., Schroff, F., Adam, H.: · 2017
Earlier work this paper cites.
Semantic scene completion from a single depth image
Song, S., Yu, F., Zeng, A., Chang, A.X., Savva, M., Funkhouser, T.: · 2017
Earlier work this paper cites.
Pyramid scene parsing network
Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: · 2017
Earlier work this paper cites.
Pointnet: Deep learning on point sets for 3d classification and segmentation
Qi, C.R., Su, H., Mo, K., Guibas, L.J.: · 2017
Earlier work this paper cites.
Pointnet++: Deep hierarchical feature learning on point sets in a metric space
Qi, C.R., Yi, L., Su, H., Guibas, L.J.: · 2017
Earlier work this paper cites.
Deep projective 3d semantic segmentation
Lawin, F.J., Danelljan, M., Tosteberg, P., Bhat, G., Khan, F.S., Felsberg, M.: · 2017
Earlier work this paper cites.
Unstructured point cloud semantic labeling using deep segmentation networks
Boulch, A., Le Saux, B., Audebert, N.: · 2017
Earlier work this paper cites.
Learning efficient object detection models with knowledge distillation
Chen, G., Choi, W., Yu, X., Han, T., Chandraker, M.: · 2017
Earlier work this paper cites.
Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer
Zagoruyko, S., Komodakis, N.: · 2017
Earlier work this paper cites.
3d semantic segmentation with submanifold sparse convolutional networks
Graham, B., Engelcke, M., van der Maaten, L.: · 2018
Earlier work this paper cites.
Understanding convolution for semantic segmentation
Wang, P., Chen, P., Yuan, Y., Liu, D., Huang, Z., Hou, X., Cottrell, G.: · 2018
Earlier work this paper cites.
Ocnet: Object context network for scene parsing
Yuan, Y., Huang, L., Guo, J., Zhang, C., Chen, X., Wang, J.: · 2018
Earlier work this paper cites.
Pointwise convolutional neural networks
Hua, B.S., Tran, M.K., Yeung, S.K.: · 2018
Earlier work this paper cites.
Tangent convolutions for dense prediction in 3d
Tatarchenko, M., Park, J., Koltun, V., Zhou, Q.Y.: · 2018
Earlier work this paper cites.
Squeezeseg: Convolutional neural nets with recurrent crf for real-time road-object segmentation from 3d lidar point cloud
Wu, B., Wan, A., Yue, X., Keutzer, K.: · 2018
Cited alongside, same era.
Knowledge transfer with jacobian matching
Srinivas, S., Fleuret, F.: · 2018
Cited alongside, same era.
Rgb-based 3d hand pose estimation via privileged learning with depth images
Yuan, S., Stenger, B., Kim, T.K.: · 2018
Cited alongside, same era.
Ccnet: Criss-cross attention for semantic segmentation
Huang, Z., Wang, X., Huang, L., Huang, C., Wei, Y., Liu, W.: · 2019
Cited alongside, same era.
Rgb and lidar fusion based 3d semantic segmentation for autonomous driving
El Madawi, K., Rashed, H., El Sallab, A., Nasr, O., Kamel, H., Yogamani, S.: · 2019
Cited alongside, same era.
Cylinder3d: An effective 3d framework for driving-scene lidar semantic segmentation
Zhou, H., Zhu, X., Song, X., Ma, Y., Wang, Z., Li, H., Lin, D.: · 2020
Later among the works it cites.
Fuseseg: Lidar point cloud segmentation fusing multi-modal data
Krispel, G., Opitz, M., Waltner, G., Possegger, H., Bischof, H.: · 2020
Later among the works it cites.
Knowledge as priors: Cross-modal knowledge generalization for datasets without superior knowledge
Zhao, L., Peng, X., Chen, Y., Kapadia, M., Metaxas, D.N.: · 2020
Later among the works it cites.
Alonso, I., Riazuelo, L., Montesano, L., Murillo, A.C.: · 2020
Later among the works it cites.
Polarnet: An improved grid representation for online lidar point clouds semantic segmentation
Zhang, Y., Zhou, Z., David, P., Yue, X., Xi, Z., Gong, B., Foroosh, H.: · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Behley, J., Garbade, M., Milioto, A., Quenzel, J., Behnke, S., Stachniss, C., Gall, J.: · 2019
Cited alongside, same era.
Dynamic graph cnn for learning on point clouds
Wang, Y., Sun, Y., Liu, Z., Sarma, S.E., Bronstein, M.M., Solomon, J.M.: · 2019
Cited alongside, same era.
Pointconv: Deep convolutional networks on 3d point clouds
Wu, W., Qi, Z., Fuxin, L.: · 2019
Cited alongside, same era.
Relation-shape convolutional neural network for point cloud analysis
Liu, Y., Fan, B., Xiang, S., Pan, C.: · 2019
Cited alongside, same era.
Kpconv: Flexible and deformable convolution for point clouds
Thomas, H., Qi, C.R., Deschaud, J.E., Marcotegui, B., Goulette, F., Guibas, L.J.: · 2019
Cited alongside, same era.
Squeezesegv2: Improved model structure and unsupervised domain adaptation for road-object segmentation from a lidar point cloud
Wu, B., Zhou, X., Zhao, S., Yue, X., Keutzer, K.: · 2019
Cited alongside, same era.
Sensor fusion for joint 3d object detection and semantic segmentation
Meyer, G.P., Charland, J., Hegde, D., Laddha, A., Vallespi-Gonzalez, C.: · 2019
Cited alongside, same era.
Later among the works it cites.
Lidar-based recurrent 3d semantic segmentation with temporal memory alignment
Duerr, F., Pfaller, M., Weigel, H., Beyerer, J.: · 2020
Later among the works it cites.
Sparse single sweep lidar point cloud segmentation via learning contextual shape priors from scene completion
Yan, X., Gao, J., Li, J., Zhang, R., Li, Z., Huang, R., Cui, S.: · 2021
Later among the works it cites.
Cylindrical and asymmetrical 3d convolution networks for lidar segmentation
Zhu, X., Zhou, H., Wang, T., Hong, F., Ma, Y., Li, W., Li, H., Lin, D.: · 2021
Later among the works it cites.
Box-aware feature enhancement for single object tracking on point clouds
Zheng, C., Yan, X., Gao, J., Zhao, W., Zhang, W., Li, Z., Cui, S.: · 2021
Later among the works it cites.
Perception-aware multi-sensor fusion for 3d lidar semantic segmentation
Zhuang, Z., Li, R., Jia, K., Wang, Q., Li, Y., Tan, M.: · 2021
Later among the works it cites.
Point transformer
Zhao, H., Jiang, L., Jia, J., Torr, P.H., Koltun, V.: · 2021
Later among the works it cites.
Point transformer
Engel, N., Belagiannis, V., Dietmayer, K.: · 2021
Later among the works it cites.
Af2-s3net: Attentive feature fusion with adaptive feature selection for sparse semantic segmentation network
Cheng, R., Razani, R., Taghavi, E., Li, E., Liu, B.: · 2021
Later among the works it cites.
Rpvnet: A deep and efficient range-point-voxel fusion network for lidar point cloud segmentation
Xu, J., Zhang, R., Dou, J., Zhu, Y., Sun, J., Pu, S.: · 2021
Later among the works it cites.
3d-to-2d distillation for indoor scene parsing
Liu, Z., Qi, X., Fu, C.W.: · 2021
Later among the works it cites.
Learning from 2d: Pixel-to-point knowledge transfer for 3d pretraining
Liu, Y.C., Huang, Y.K., Chiang, H.Y., Su, H.T., Liu, Z.Y., Chen, C.T., Tseng, C.Y., Hsu, W.H.: · 2021
Later among the works it cites.
Image2point: 3d point-cloud understanding with pretrained 2d convnets
Xu, C., Yang, S., Zhai, B., Wu, B., Yue, X., Zhan, W., Vajda, P., Keutzer, K., Tomizuka, M.: · 2021
Later among the works it cites.
Learning 3d semantic segmentation with only 2d image supervision
Genova, K., Yin, X., Kundu, A., Pantofaru, C., Cole, F., Sud, A., Brewington, B., Shucker, B., Funkhouser, T.: · 2021
Later among the works it cites.
Revisiting knowledge distillation: An inheritance and exploration framework
Huang, Z., Shen, X., Xing, J., Liu, T., Tian, X., Li, H., Deng, B., Huang, J., Hua, X.S.: · 2021
Later among the works it cites.
Knowledge distillation via softmax regression representation learning
Jing Yang, Brais Martinez, A.B.G.T.: · 2021
Later among the works it cites.
Beyond 3d siamese tracking: A motion-centric paradigm for 3d single object tracking in point clouds
Zheng, C., Yan, X., Zhang, H., Wang, B., Cheng, S., Cui, S., Li, Z.: · 2022
Closest in time.
X-trans2cap: Cross-modal knowledge transfer using transformer for 3d dense captioning
Yuan, Z., Yan, X., Liao, Y., Guo, Y., Li, G., Cui, S., Li, Z.: · 2022
Closest in time.