The DOI matches its Crossref record; 63 of the 65 references it cites were found in a registry or an index.
Perception-Aware Multi-Sensor Fusion for 3D LiDAR Semantic Segmentation
Abstract
3D LiDAR (light detection and ranging) semantic segmentation is important in scene understanding for many applications, such as auto-driving and robotics. For example, for autonomous cars equipped with RGB cameras and LiDAR, it is crucial to fuse complementary information from different sensors for robust and accurate segmentation. Existing fusion-based methods, however, may not achieve promising performance due to the vast difference between the two modalities. In this work, we investigate a collaborative fusion scheme called perception-aware multi-sensor fusion (PMF) to exploit perceptual information from two modalities, namely, appearance information from RGB images and spatio-depth information from point clouds. To this end, we first project point clouds to the camera coordinates to provide spatio-depth information for RGB images. Then, we propose a two-stream network to extract features from the two modalities, separately, and fuse the features by effective residual-based fusion modules. Moreover, we propose additional perception-aware losses to measure the perceptual difference between the two modalities. Extensive experiments on two benchmark data sets show the superiority of our method. For example, on nuScenes, our PMF outperforms the state-of-the-art method by 0.8% in mIoU.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“| PMF [107] | ResNet34 [16] | 63.9 |”
From Multimodal semantic segmentation in autonomous driving: A review of current approaches and future perspectives (2022) · cites this paper as [107]
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al. (2019) · arXiv · doi:10.48550/arxiv.1912.01703 · Cited by 31,473
Focal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.324 · Cited by 30,581
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2699184 · Cited by 22,392
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2016) · arXiv · doi:10.48550/arxiv.1606.00915 · Cited by 22,392
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2644615 · Cited by 19,631
Zhao, H., Shi, J., Qi, X., Wang, X., & Jia, J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.660 · Cited by 16,438
Are we ready for autonomous driving? The KITTI vision benchmark suite
Geiger, A., Lenz, P., & Urtasun, R. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248074 · Cited by 14,897
Research works printed but not linked: 3
- xu, 2020, Generative lowbitwidth data free quantization, European Conference on Computer Vision, 1
- geiger, 2012, A toolbox for automatic calibration of range and camera sensors using a single shot, International Conference on Robotics and Automation
- nesterov, 1983, A method for solving the convex programming problem with convergence rate o (1/kˆ 2), Proceedings of the USSR Academy of Sciences, 269, 543
Cited by
Sorted by citations
CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation With Transformers
Zhang, J., Liu, H., Yang, K., Hu, X., Liu, R., & Stiefelhagen, R. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3300537 · Cited by 731
2DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point Clouds
Yan, X., Gao, J., Zheng, C., Zheng, C., Zhang, R., Cui, S. R., & Li, Z. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19815-1_39 · Cited by 301
Spherical Transformer for LiDAR-Based 3D Recognition
Lai, X. Q., Chen, Y., Lu, F., Liu, J., & Jia, J. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01683 · Cited by 248
Muhammad, K., Hussain, T., Ullah, H., Ser, J. D., Rezaei, M., Kumar, N., Hijji, M., Bellavista, P., & de Albuquerque, V. H. C. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3207665 · Cited by 232
Delivering Arbitrary-Modal Semantic Segmentation
Zhang, J., Liu, R., Shi, H., Yang, K., Reiß, S., Peng, K., Fu, H., Wang, K., & Stiefelhagen, R. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00116 · Cited by 215
Multi-Sensor Fusion and Cooperative Perception for Autonomous Driving: A Review
Xiang, C. J., Chen, F., Xie, X., Shi, B., Lü, H., Lv, Y., Yang, M., & Niu, Z. (2023) · IEEE Intelligent Transportation Systems Magazine · doi:10.1109/mits.2023.3283864 · Cited by 208
RangeViT: Towards Vision Transformers for 3D Semantic Segmentation in Autonomous Driving
Ando, A., Gidaris, S., Bursuc, A., Puy, G., Boulch, A., & Marlet, R. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00507 · Cited by 165
MSeg3D: Multi-Modal 3D Semantic Segmentation for Autonomous Driving
Li, J., Dai, H., Han, H., & Ding, Y. S. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.02078 · Cited by 137
SDSTrack: Self-Distillation Symmetric Adapter Learning for Multi-Modal Visual Object Tracking
Hou, X., Xing, J., Qian, Y., Guo, Y., Xin, S., Chen, J., Tang, K., Wang, M., Jiang, Z., Liu, L., et al. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.02507 · Cited by 143
Sarker, S., Sarker, P., Stone, G., Gorman, R., Tavakkoli, A., Bebis, G. N., & Sattarvand, J. (2024) · Machine Vision and Applications · doi:10.1007/s00138-024-01543-1 · Cited by 101
UniSeg: A Unified Multi-Modal LiDAR Segmentation Network and the OpenPCSeg Codebase
Liu, Y., Chen, R., Li, X. S., Kong, L., Yang, Y., Xia, Z., Bai, Y., Zhu, X., Ma, Y., Li, Y., et al. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01980 · Cited by 80
Self-citationGrowSP: Unsupervised Semantic Segmentation of 3D Point Clouds
Zhang, Z., Yang, B., Wang, B., & Li, B. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01690 · Cited by 71
Yang, B., Li, J., & Zeng, T. (2025) · World Electric Vehicle Journal · doi:10.3390/wevj16010020 · Cited by 76
Vehicle-Road-Cloud Collaborative Perception Framework and Key Technologies: A Review
Gao, B., Liu, J., Zou, H., Chen, J., He, L., & Li, K. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3459799 · Cited by 65
Rizzoli, G., Barbato, F., & Zanuttigh, P. (2022) · Technologies · doi:10.3390/technologies10040090 · Cited by 58
A Survey of the Multi-Sensor Fusion Object Detection Task in Autonomous Driving
Wang, H., Liu, J., Dong, H., & Shao, Z. (2025) · Sensors · doi:10.3390/s25092794 · Cited by 67
Meta-RangeSeg: LiDAR Sequence Semantic Segmentation Using Multiple Feature Aggregation
Wang, S., Zhu, J., & Zhang, R. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3191040 · Cited by 54
SVASeg: Sparse Voxel-Based Attention for 3D LiDAR Point Cloud Semantic Segmentation
Zhao, L. F., Xu, S., Liu, L., Ming, D., & Tao, W. (2022) · Remote Sensing · doi:10.3390/rs14184471 · Cited by 54
EPMF: Efficient Perception-Aware Multi-Sensor Fusion for 3D Semantic Segmentation
Tan, M., Zhuang, Z., Chen, S., Li, R., Jia, K., Wang, Q., & Li, Y. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3402232 · Cited by 50
Self-citationDeep learning for LiDAR-only and LiDAR-fusion 3D perception: a survey
Wu, D., Liang, Z., & Chen, G. (2022) · Intelligence & Robotics · doi:10.20517/ir.2021.20 · Cited by 51
Few-shot semantic segmentation: a review on recent approaches
Chang, Z., Lu, Y., Ran, X., Gao, X., & Wang, X. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08758-9 · Cited by 45
HRFuser: A Multi-Resolution Sensor Fusion Architecture for 2D Object Detection
Brödermann, T., Sakaridis, C., Dai, D., & Gool, L. V. (2023) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc57777.2023.10422432 · Cited by 46
BSSNet: A Real-Time Semantic Segmentation Network for Road Scenes Inspired From AutoEncoder
Shi, X., Yin, Z., Han, G., Liu, W., Qin, L., Bi, Y., & Li, S. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3325360 · Cited by 45
A Multi-Phase Camera-LiDAR Fusion Network for 3D Semantic Segmentation With Weak Supervision
Chang, X., Pan, H., Sun, W., & Gao, H. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3241641 · Cited by 44
CalibDepth: Unifying Depth Map Representation for Iterative LiDAR-Camera Online Calibration
Zhu, J., Xue, J., & Zhang, P. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10161575 · Cited by 46
CPCM: Contextual Point Cloud Modeling for Weakly-supervised Point Cloud Semantic Segmentation
Liu, L., Zhuang, Z., Huang, S., Xiao, X., Xiang, T., Chen, C., Wang, J., & Tan, M. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01688 · Cited by 40
Self-citationDXQ-Net: Differentiable LiDAR-Camera Extrinsic Calibration Using Quality-aware Flow
Jing, X., Ding, X., Xiong, R., Deng, H., & Wang, Y. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981418 · Cited by 36
Joint Semantic Segmentation using representations of LiDAR point clouds and camera images
Wu, Y., Liu, J., Gong, M., Miao, Q., Ma, W., & Xu, C. X. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102370 · Cited by 33
Grounding 3D Object Affordance from 2D Interactions in Images
Yang, Y., Zhai, W., Luo, H., Cao, Y. H., Luo, J., & Zha, Z.-J. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01001 · Cited by 32
Li, M., Zhang, Y., Xie, Y., Gao, Z., Li, C., Zhang, Z., & Qu, Y. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3547990 · Cited by 31
Image Understands Point Cloud: Weakly Supervised 3D Semantic Segmentation via Association Learning
Sun, T., Zhang, Z., Tan, X., Qu, Y., & Xie, Y. (2024) · IEEE Transactions on Image Processing · doi:10.1109/tip.2024.3372449 · Cited by 29
See More and Know More: Zero-shot Point Cloud Segmentation via Multi-modal Visual Data
Lu, Y., Jiang, Q., Chen, R., Hou, Y., Zhu, X., & Ma, Y. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01981 · Cited by 29
Fusion of hierarchical class graphs for remote sensing semantic segmentation
Kang, X., Hong, Y., Duan, P., & Shutao Li, A. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102409 · Cited by 30
Integrating Multi-Modal Sensors: A Review of Fusion Techniques for Intelligent Vehicles
Wei, C., Qin, Z., Zhang, Z., Wu, G., & Barth, M. (2025) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv64158.2025.11097396 · Cited by 38
Variational Distillation for Multi-View Learning
Tian, X., Zhang, Z., Wang, C., Zhang, W., Qu, Y., Ma, L., Wu, Z., Xie, Y., & Tao, D. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3343717 · Cited by 27
Multi-Space Alignments Towards Universal LiDAR Segmentation
Liu, Y., Kong, L., Wu, X., Chen, R., Li, X. Y., Pan, L., Liu, Z., & Ma, Y. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01388 · Cited by 26
Advancements in perception system with multi-sensor fusion for embodied agents
Du, H., Ren, L., Wang, Y., Cao, X. S., & Sun, C. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102859 · Cited by 30
Cen, J., Zhang, S., Pei, Y., Li, K., Zheng, H., Luo, M., Zhang, Y., & Chen, Q. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3335771 · Cited by 26
LiDAR2Map: In Defense of LiDAR-Based Semantic Map Construction Using Online Camera Distillation
Wang, S., Li, W., Liu, W., Liu, X., & Zhu, J. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00502 · Cited by 25
3D Semantic Segmentation of Aerial Photogrammetry Models Based on Orthographic Projection
Rong, M., & Shen, S. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3273224 · Cited by 28
Multi-Modal Contrastive Learning for LiDAR Point Cloud Rail-Obstacle Detection in Complex Weather
Wen, L., Peng, Y., Lin, M., Gan, N., & Tan, R. (2024) · Electronics · doi:10.3390/electronics13010220 · Cited by 25
WildScenes: A benchmark for 2D and 3D semantic segmentation in large-scale natural environments
Vidanapathirana, K., Knights, J., Hausler, S., Cox, M., Ramezani, M., Jooste, J., Griffiths, E., Mohamed, S., Sridharan, S., Fookes, C., et al. (2024) · The International Journal of Robotics Research · doi:10.1177/02783649241278369 · Cited by 29
MiLNet: Multiplex Interactive Learning Network for RGB-T Semantic Segmentation
Liu, J., Liu, H., Li, X., Ren, J., & Xu, X. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3544484 · Cited by 23
Long, N., Han, Y., Wang, L., Li, H., & Yang, Q. (2022) · Sensors · doi:10.3390/s22072453 · Cited by 24
Wu, Y., Xing, M., Zhang, Y., Xie, Y., Jianping Fan, B., Shi, Z., & Qu, Y. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3612013 · Cited by 22
LiDAR-Camera Panoptic Segmentation via Geometry-Consistent and Semantic-Aware Alignment
Zhang, Z., Zhang, Z., Yu, Q., Yi, R., Xie, Y., & Ma, L. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00339 · Cited by 24
Wu, Z., Zhang, Y., Lan, R., Qiu, S., Ran, S., & Liu, Y. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123990 · Cited by 20
VPA-Net: A visual perception assistance network for 3d lidar semantic segmentation
Lin, F., Lin, T., Yao, Y. C., Ren, H., Wu, J., & Cai, Q. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.111014 · Cited by 20
Robust 3D Semantic Segmentation Based on Multi-Phase Multi-Modal Fusion for Intelligent Vehicles
Ni, P., Li, X., Xu, W., Kong, D., Hu, Y., & Wei, K. (2023) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2023.3317784 · Cited by 21
TFNet: Exploiting Temporal Cues for Fast and Accurate LiDAR Semantic Segmentation
Li, R., Li, S., Chen, X., Ma, T., Gall, J., & Liang, J. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00457 · Cited by 22
Self-citationFree Space Detection Using Camera-LiDAR Fusion in a Bird’s Eye View Plane
Yu, B.-J., Lee, D., Lee, J.-S., & Kee, S.-C. (2021) · Sensors · doi:10.3390/s21227623 · Cited by 19
Uni-to-Multi Modal Knowledge Distillation for Bidirectional LiDAR-Camera Semantic Segmentation
Sun, T., Zhang, Z., Tan, X., Peng, Y. L., Qu, Y., & Xie, Y. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3451658 · Cited by 18
JPerceiver: Joint Perception Network for Depth, Pose and Layout Estimation in Driving Scenes
Zhao, H., Zhang, J., Zhang, S., & Tao, D. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19839-7_41 · Cited by 19
Mahima, K. T. Y., Perera, A. G., Anavatti, S. G., & Garratt, M. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3456293 · Cited by 19
An, P., Gao, Y., Wang, L., Chen, Y., & Ma, J. (2022) · Remote Sensing · doi:10.3390/rs14112525 · Cited by 17
Song, H., Cho, J., Ha, J., Park, J., & Jo, K. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123950 · Cited by 17
A Novel, Efficient and Accurate Method for Lidar Camera Calibration
Huang, Z., Zhang, X., García, A., & Huang, X. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10611162 · Cited by 17
Learning Motion and Temporal Cues for Unsupervised Video Object Segmentation
Zhuge, Y., Gu, H., Zhang, L., Qi, J., & Lu, H. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3418980 · Cited by 16
Zhang, A., Li, S., Wu, J., Li, S., & Zhang, B. (2023) · IEEE Access · doi:10.1109/access.2023.3287940 · Cited by 18
Synchronize Feature Extracting and Matching: A Single Branch Framework for 3D Object Tracking
Ma, T., Wang, M., Xiao, J., Wu, H., & Liu, Y. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00913 · Cited by 16
ProtoTransfer: Cross-Modal Prototype Transfer for Point Cloud Segmentation
Pin, T., Xu, H., & Ma, C. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00309 · Cited by 16
Pseudo-LiDAR for Visual Odometry
Miao, Y., Deng, H., Jiang, C., Feng, Z., Wu, X., Wang, G., & Wang, H. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3315416 · Cited by 15
SFPNet: Sparse Focal Point Network for Semantic Segmentation on General LiDAR Point Clouds
Wang, Y., Zhao, W., Cao, C., Deng, T., Wang, J., & Chen, W. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72652-1_24 · Cited by 16
Li, Q., & Zhuang, Y. (2023) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2023.103488 · Cited by 14
MRCNet: Multiresolution LiDAR-Camera Calibration Using Optical Center Distance Loss Network
Wang, H., Wang, Z., Yu, G., Yang, S., & Yang, Y. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3328267 · Cited by 14
TPV-IGKD: Image-Guided Knowledge Distillation for 3D Semantic Segmentation With Tri-Plane-View
Li, J.-C., Lu, J., Wei, M., Kang, H.-Y., & Zhang, Q. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3361163 · Cited by 14
LiDAR in Connected and Autonomous Vehicles: Perception, Threat Model, and Defense
Khan, M. A., Menouar, H., Abdallah, M. M., & Abu‐Dayya, A. (2024) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2024.3510787 · Cited by 19
SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics
Hayeon, O., Yang, C., & Huh, K. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0969-7_13 · Cited by 16
A Cross-Modal Attention-Driven Multi-Sensor Fusion Method for Semantic Segmentation of Point Clouds
Shi, H., Wang, X., Zhao, J., & Hua, X. (2025) · Sensors · doi:10.3390/s25082474 · Cited by 14
DAPS3D: Domain Adaptive Projective Segmentation of 3D LiDAR Point Clouds
Klokov, A., Pak, D. U., Khorin, A., Yudin, D. A., Kochiev, L., Luchinskiy, V. D., & Bezuglyj, V. (2023) · IEEE Access · doi:10.1109/access.2023.3298706 · Cited by 13
Zhuang, L., Jiang, T., Jiang, H., Wang, A., & Huang, Z. (2023) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2023.3342296 · Cited by 13
Hybrid Cross-Transformer-KPConv for Point Cloud Segmentation
Wen, S., Li, P., & Zhang, H. (2023) · IEEE Signal Processing Letters · doi:10.1109/lsp.2023.3343253 · Cited by 13
LiDAR-Camera Continuous Fusion in Voxelized Grid for Semantic Scene Completion
Lu, Z., Cao, B., & Hu, Q. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3435045 · Cited by 15
CSFNet: Cross-Modal Semantic Focus Network for Semantic Segmentation of Large-Scale Point Clouds
Luo, Y., Han, T., Liu, Y., Su, J., Chen, Y., Li, J., Wu, Y., & Cai, G. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3535800 · Cited by 13
ShareCMP: Polarization-Aware RGB-P Semantic Segmentation
Liu, Z., Wang, B., Wang, L., Chenyu, M., & Li, Y. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3570764 · Cited by 17
Self-citationUFO: Uncertainty-aware LiDAR-image Fusion for Off-road Semantic Terrain Map Estimation
Kim, O., Seo, J., Ahn, S., & Kim, C. H. (2024) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv55156.2024.10588787 · Cited by 12
Zhang, Y., Wu, Z., Lan, R., Liang, Y., & Liu, Y. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112654 · Cited by 12
HENet: Hybrid Encoding for End-to-End Multi-task 3D Perception from Multi-view Cameras
Xia, Z., Lin, Z., Wang, X., Wang, Y., Xing, Y., Qi, S., Dong, N., & Yang, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72973-7_22 · Cited by 12
Small Object Detection on the Water Surface Based on Radar and Camera Fusion
Wei, Q., Jiang, X., Liu, Y., Su, Q., & Yu, M. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10446880 · Cited by 11
2D Feature Distillation for Weakly- and Semi-Supervised 3D Semantic Segmentation
Unal, O., Dai, D., Hoyer, L., Can, Y. B., & Gool, L. V. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00717 · Cited by 11
MVG-Net: LiDAR Point Cloud Semantic Segmentation Network Integrating Multi-View Images
Liu, Y., Liu, Y., & Duan, Y. (2024) · Remote Sensing · doi:10.3390/rs16152821 · Cited by 11
Geometry-Injected Image-Based Point Cloud Semantic Segmentation
Shuai, H., & Liu, Q. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3264292 · Cited by 11
Akwensi, P. H., Kang, Z., & Wang, R. (2023) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2023.103302 · Cited by 10
Zhang, S., & Xie, M. (2024) · Frontiers in Physics · doi:10.3389/fphy.2024.1411559 · Cited by 10
RAPiD-Seg: Range-Aware Pointwise Distance Distribution Networks for 3D LiDAR Segmentation
Li, L., Shum, H. P. H., & Breckon, T. P. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72667-5_13 · Cited by 11
A Taxonomy of Semantic Information in Robot-Assisted Disaster Response
Ruan, T., Wang, H., Stolkin, R., & Chiou, M. (2022) · IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) · doi:10.1109/ssrr56537.2022.10018727 · Cited by 10
DefFusion: Deformable Multimodal Representation Fusion for 3D Semantic Segmentation
Xu, R., Wang, C., Zhang, D., Zhang, M., Xu, S., Meng, W., & Zhang, X. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610465 · Cited by 10
Multisensor Fusion Network for Unstructured Scene Segmentation With Surface Normal Incorporated
Feng, Y., Li, X., Ni, P., Liu, X., & Jiang, T. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3370255 · Cited by 9
Duan, Y., Meng, L., Meng, Y., Zhu, J., Zhang, J., Zhang, J., & Liu, X. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3472751 · Cited by 9
Wu, Y., Xing, M., Zhang, Y., Xie, Y., & Qu, Y. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3545971 · Cited by 9
RC-AutoCalib: An End-to-End Radar-Camera Automatic Calibration Network
Luu, V.-T., Cai, Y.-L., Tran, V.-H., Chiu, W.-C., Chen, Y., & Huang, C.-C. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.00628 · Cited by 9
Ni, P., Li, X., Kong, D., Wei, K., & Hu, Y. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3329389 · Cited by 9
DFAMNet: dual fusion attention multi-modal network for semantic segmentation on LiDAR point clouds
Li, M., Wang, G., Zhu, M., Li, C., Liu, H., Pan, X., & Qian, L. (2024) · Applied Intelligence · doi:10.1007/s10489-024-05302-7 · Cited by 9
Continual Road-Scene Semantic Segmentation Via Feature-Aligned Symmetric Multi-Modal Network
Barbato, F., Camuffo, E., Milani, S., & Zanuttigh, P. (2024) · Proceedings - International Conference on Image Processing · doi:10.1109/icip51287.2024.10647930 · Cited by 9
Multi-Modal LiDAR Point Cloud Semantic Segmentation with Salience Refinement and Boundary Perception
Zhou, Y. Z., Xie, Z., Zhao, J., Du, W., Yao, R., & Saddik, A. E. (2024) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3674979 · Cited by 8
Xu, Z., Lyu, Y., Zhou, J., & Wang, L. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72890-7_12 · Cited by 8
SAMNet++: A Segment Anything Model for Supervised 3D Point Cloud Semantic Segmentation
Shahraki, M., Elamin, A., & El‐Rabbany, A. (2025) · Remote Sensing · doi:10.3390/rs17071256 · Cited by 8
Local-consistent Transformation Learning for Rotation-invariant Point Cloud Analysis
Chen, Y., Duan, L., Zhao, S., Ding, C., & Tao, D. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00518 · Cited by 8
Lin, Z., Peng, X., Cong, P., Ge, Z., Sun, Y., Hou, Y., Zhu, X., Yang, S., & Ma, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72952-2_26 · Cited by 8
Learning Modality-Agnostic Representation for Semantic Segmentation from Any Modalities
Xu, Z., Lyu, Y., & Wang, L. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72754-2_9 · Cited by 14
Metadata
- Authors
- Zhuang, Z., Li, R., Jia, K., Wang, Q., Li, Y., & Tan, M.
- Affiliations
- South China University of Technology (CN)
- Shenzhen Youjia Innov Tech Co., Ltd
- Venue
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2021-10
- Pages
- 16260-16270
- OpenAlex
- W3173732446
- Cited by
- 216
- References
- 65
- Citations by year
- 2026: 26 · 2025: 58 · 2024: 68 · 2023: 47 · 2022: 16 · 2021: 1
- FWCI
- 10.79
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- Funders
- Research and Development
- Ministry of Science and Technology
- Fundamental Research Funds for the Central Universities
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › 3D Shape Modeling and Analysis
- Keywords
- multi-sensor fusion, RGB-LiDAR fusion, point cloud projection, two-stream network, autonomous driving, nuScenes
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
