PointPainting: Sequential Fusion for 3D Object Detection
Open accessAbstract
Camera and lidar are important sensor modalities for robotics in general and self-driving cars in particular. The sensors provide complementary information offering an opportunity for tight sensor-fusion. Surprisingly, lidar-only methods outperform fusion methods on the main benchmark datasets, suggesting a gap in the literature. In this work, we propose PointPainting: a sequential fusion method to fill this gap. PointPainting works by projecting lidar points into the output of an image-only semantic segmentation network and appending the class scores to each point. The appended (painted) point cloud can then be fed to any lidar-only method. Experiments show large improvements on three different state-of-the art methods, Point-RCNN, VoxelNet and PointPillars on the KITTI and nuScenes datasets. The painted version of PointRCNN represents a new state of the art on the KITTI leaderboard for the bird's-eye view detection task. In ablation, we study how the effects of Painting depends on the quality and format of the semantic segmentation output, and demonstrate how latency can be minimized through pipelining.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
BEVFormer's performance on the nuScenes test set is on par with LiDAR-based baselines.
“and on par with the performance of LiDAR-based baselines”
From BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers (2022) · cites this paper as [43]
Table 4 (nuScenes test results; page unavailable in our copy) reports performance for LiDAR-only baselines, including PointPillars+ and MEGVII. The paper does not report BEVFormer's performance or compare it with those baselines, so it cannot establish that BEVFormer is on par with them. (LLM, conf=0.86)
Experimental — accuracy not yet measuredBeing still the only annotated AV dataset to provide radar data, nuScenes encourages researchers to explore radar and sensor fusion for object detection.
“Being still the only annotated A V dataset to provide radar data, nuScenes encourages researchers to explore radar and sensor fusion for object detection [27, 42, 72].”
From nuScenes: A Multimodal Dataset for Autonomous Driving (2020) · cites this paper as [72]
The dataset description (KITTI section; page unavailable) says nuScenes has 3D bounding-box annotations and synchronized lidar, cameras, and radars. The Abstract and Introduction describe camera–lidar fusion for object detection, but the paper does not say nuScenes is the only annotated AV dataset with radar or describe its work as radar fusion. (LLM, conf=0.86)
Experimental — accuracy not yet measuredDeepInteraction addresses limitation of unilateral fusion by introducing multimodal interaction encoder and decoder.
“DeepInteraction [86] addresses the limitation of unilateral fusion by introducing a multimodal interaction encoder and decoder.”
From Multimodal fusion and vision–language models: A survey for robot vision (2026) · cites this paper as [86]
The Introduction and Contributions describe PointPainting, a sequential fusion method that augments lidar points with image semantics. Our copy does not discuss DeepInteraction or a multimodal interaction encoder and decoder; searches for those terms found no supporting passage. (LLM, conf=0.76)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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 · see citing works
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 · see citing works
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., & Adam, H. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01234-2_49 · Cited by 20,054 · see citing works
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 · see citing works
The Cityscapes Dataset for Semantic Urban Scene Understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., & Schiele, B. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.350 · Cited by 12,433 · see citing works
nuScenes: A Multimodal Dataset for Autonomous Driving
Caesar, H., Bankiti, V., Lang, A. H., Vora, S., Liong, V. E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., & Beijbom, O. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01164 · Cited by 7,152 · see citing works
Self-citationVoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
Zhou, Y., & Tuzel, O. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00472 · Cited by 4,813 · see citing works
PointPillars: Fast Encoders for Object Detection From Point Clouds
Lang, A. H., Vora, S., Caesar, H., Zhou, L., Yang, J., & Beijbom, O. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.01298 · Cited by 4,673 · see citing works
Self-citationMulti-view 3D Object Detection Network for Autonomous Driving
Chen, X., Ma, H., Wan, J., Li, B., & Xia, T. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.691 · Cited by 3,450 · see citing works
On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., & Weinberger, K. Q. (2017) · arXiv · doi:10.48550/arxiv.1706.04599 · Cited by 3,435 · see citing works
SECOND: Sparsely Embedded Convolutional Detection
Yan, Y., Mao, Y., & Li, B. (2018) · Sensors · doi:10.3390/s18103337 · Cited by 3,352 · see citing works
PointRCNN: 3D Object Proposal Generation and Detection From Point Cloud
Shi, S., Wang, X., & Li, H. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00086 · Cited by 3,093 · see citing works
Frustum PointNets for 3D Object Detection from RGB-D Data
Qi, C. R., Liu, W., Wu, C., Su, H., & Guibas, L. J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00102 · Cited by 2,655 · see citing works
Joint 3D Proposal Generation and Object Detection from View Aggregation
Ku, J., Mozifian, M., Lee, J., Harakeh, A., & Waslander, S. L. (2018) · 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8594049 · Cited by 1,697 · see citing works
The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes
Neuhold, G., Ollmann, T., Bulo, S. R., & Kontschieder, P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.534 · Cited by 1,434 · see citing works
PIXOR: Real-time 3D Object Detection from Point Clouds
Yang, B., Luo, W., & Urtasun, R. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00798 · Cited by 1,249 · see citing works
Wang, Y., Chao, W.-L., Garg, D., Hariharan, B., Campbell, M., & Weinberger, K. Q. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00864 · Cited by 1,154 · see citing works
STD: Sparse-to-Dense 3D Object Detector for Point Cloud
Yang, Z., Sun, Y., Liu, S., Shen, X., & Jia, J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00204 · Cited by 875 · see citing works
Smith, L. N. (2018) · arXiv · doi:10.48550/arxiv.1803.09820 · Cited by 790 · see citing works
Vehicle Detection from 3D Lidar Using Fully Convolutional Network
Li, B., Zhang, T., & Xia, T. (2016) · Robotics: Science and Systems XII · doi:10.15607/rss.2016.xii.042 · Cited by 577 · see citing works
Wang, Z., & Jia, K. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8968513 · Cited by 547 · see citing works
Disentangling Monocular 3D Object Detection
Simonelli, A., Bulo, S. R., Porzi, L., Lopez-Antequera, M., & Kontschieder, P. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00208 · Cited by 520 · see citing works
Improving Semantic Segmentation via Video Propagation and Label Relaxation
Zhu, Y., Sapra, K., Reda, F. A., Shih, K. J., Newsam, S. D., Tao, A., & Catanzaro, B. (2019) · 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/cvpr.2019.00906 · Cited by 429 · see citing works
Deep Continuous Fusion for Multi-Sensor 3D Object DetectionPreprint
Liang, M. X., Yang, B., Wang, S., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.10992 · Cited by 428 · see citing works
LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving
Meyer, G. P., Laddha, A., Kee, E., Vallespi-Gonzalez, C., & Wellington, C. K. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.01296 · Cited by 374 · see citing works
Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection
Zhu, B., Jiang, Z., Zhou, X., Li, Z., & Yu, G. (2019) · arXiv · doi:10.48550/arxiv.1908.09492 · Cited by 297 · see citing works
Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud
Weng, X., & Kitani, K. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00114 · Cited by 290 · see citing works
Wang, Z., & Jia, K. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.01864 · Cited by 159 · see citing works
Sensor Fusion for Joint 3D Object Detection and Semantic Segmentation
Meyer, G. P., Charland, J., Hegde, D. K., Laddha, A., & Vallespi-Gonzalez, C. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00162 · Cited by 158 · see citing works
SDC-Net: Video Prediction Using Spatially-Displaced Convolution
Reda, F. A., Liu, G., Shih, K. J., Kirby, R. M., Barker, J., Tarjan, D., Tao, A., & Catanzaro, B. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01234-2_44 · Cited by 138 · see citing works
IPOD: Intensive Point-based Object Detector for Point Cloud
Yang, Z., Sun, Y., Liu, S., Shen, X., & Jia, J. (2018) · arXiv · doi:10.48550/arxiv.1812.05276 · Cited by 132 · see citing works
Complex-YOLO: Real-time 3D Object Detection on Point CloudsPreprint
Simón, M., Milz, S., Amende, K., & Groß, H. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.06199 · Cited by 120 · see citing works
Fusing Bird’s Eye View LIDAR Point Cloud and Front View Camera Image for 3D Object Detection
Wang, Z., Zhan, W., & Tomizuka, M. (2018) · 2018 IEEE Intelligent Vehicles Symposium (IV) · doi:10.1109/ivs.2018.8500387 · Cited by 81 · see citing works
Flagged first, then by citations
Research works printed but not linked: 4
- [4]4. B. Cheng, M. D. Collins, Y. Zhu, T. Liu, T. S. Huang, H. Adam, and L.-C. Chen. Panoptic-deeplab. ICCV Work- shop, 2019. 3
- [13]13. M. Liang, B. Yang, Y. Chen, R. Hu, and R. Urtasun. Multi- task multi-sensor fusion for 3d object detection. In CVPR, 2019. 1, 2, 5, 11
- [23]23. A. Simonelli, S. Rota Bulo, L. Porzi, M. Lopez-Antequera, and P. Kontschieder. Disentangling monocular 3d object de- tection. ICCV, 2019. 7
- [33]33. Y. You, Y. Wang, W.-L. Chao, D. Garg, G. Pleiss, B. Hari- haran, M. Campbell, and K. Q. Weinberger. Pseudo-lidar++: Accurate depth for 3d object detection in autonomous driv- ing. ICLR, 2020. 1, 2
Cited by
Sorted by citations
nuScenes: A Multimodal Dataset for Autonomous Driving
Caesar, H., Bankiti, V., Lang, A. H., Vora, S., Liong, V. E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., & Beijbom, O. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01164 · Cited by 7,152 · see citing works
“Being still the only annotated AV dataset to provide radar data, nuScenes encourages researchers to explore radar and sensor fusion for object detection [27, 42, 72].”
BackgroundSelf-citationTransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers
Bai, X., Hu, Z., Zhu, X., Huang, Q., Chen, Y., Fu, H., & Tai, C.-L. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00116 · Cited by 990 · see citing works
DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detection
Yu, H., Luo, Y., Shu, M., Huo, Y., Yang, Z., Shi, Y., Guo, Z., Li, H., Hu, X., Yuan, J., et al. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.02067 · Cited by 577 · see citing works
DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection
Li, Y., Yu, A. W., Meng, T., Caine, B., Ngiam, J., Peng, D., Shen, J., Lu, Y., Zhou, D., Le, Q. V., et al. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01667 · Cited by 563 · see citing works
Perception-Aware Multi-Sensor Fusion for 3D LiDAR Semantic Segmentation
Zhuang, Z., Li, R., Jia, K., Wang, Q., Li, Y., & Tan, M. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01597 · Cited by 216 · see citing works
Cross Modal Transformer: Towards Fast and Robust 3D Object Detection
Yan, J., Liu, Y., Sun, J., Jia, F., Li, S., Wang, T., & Zhang, X. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01675 · Cited by 182 · see citing works
Disparity-Based Multiscale Fusion Network for Transportation Detection
Chen, J., Wang, Q., Peng, W., Xu, H., Li, X., & Xu, W. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3161977 · Cited by 166 · see citing works
Learning for Vehicle-to-Vehicle Cooperative Perception Under Lossy Communication
Li, J., Xu, R., Liu, X., Ma, J., Chi, Z., Ma, J., & Yu, H. (2023) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2023.3260040 · Cited by 161 · see citing works
CRN: Camera Radar Net for Accurate, Robust, Efficient 3D Perception
Kim, Y., Shin, J., Kim, S., Lee, I.-J., Choi, J. W., & Kum, D. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01615 · Cited by 155 · see citing works
Tahir, N. U. A., Zhang, Z., Asim, M., Chen, J., & ELAffendi, M. (2024) · Algorithms · doi:10.3390/a17030103 · Cited by 145 · see citing works
CRAFT: Camera-Radar 3D Object Detection with Spatio-Contextual Fusion Transformer
Kim, Y., Kim, S., Choi, J. W., & Kum, D. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i1.25198 · Cited by 130 · see citing works
IS-Fusion: Instance-Scene Collaborative Fusion for Multimodal 3D Object Detection
Yin, J., Shen, J., Chen, R., Li, W., Yang, R., Frossard, P., & Wang, W. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01412 · Cited by 133 · see citing works
FocalFormer3D : Focusing on Hard Instance for 3D Object Detection
Chen, Y., Yu, Z., Chen, Y., Lan, S., Anandkumar, A., Jia, J., & Alvarez, J. M. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00771 · Cited by 119 · see citing works
Object Detection with Transformers: A Review
Shehzadi, T., Hashmi, K. A., Liwicki, M., Stricker, D., & Afzal, M. Z. (2025) · Sensors · doi:10.3390/s25196025 · Cited by 93 · see citing works
Emerging trends in autonomous vehicle perception: Multimodal fusion for 3D object detection
Alaba, S. Y., Gurbuz, A. C., & Ball, J. E. (2024) · World Electric Vehicle Journal · doi:10.3390/wevj15010020 · Cited by 82 · see citing works
Towards learning-based planning: The nuPlan benchmark for real-world autonomous driving
Karnchanachari, N., Geromichalos, D., Tan, K. S., Li, N., Eriksen, C., Yaghoubi, S., Mehdipour, N., Bernasconi, G., Fong, W. K., Guo, Y., et al. (2024) · 2024 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra57147.2024.10610077 · Cited by 46 · see citing works
“I N the last decade, autonomous vehicle perception and prediction have been revolutionized by deep learning-based methods trained on large-scale datasets [1], [2], [3], [4], [5], [6], [7].”
MethodMultimodal fusion and vision–language models: A survey for robot vision
Han, X., Chen, S., Fu, Z., Feng, Z., Fan, L., An, D., Wang, C., Guo, L., Meng, W., Zhang, X., et al. (2026) · Information Fusion · doi:10.1016/j.inffus.2025.103652 · Cited by 41 · see citing works
“…and FV (Front View) with RGB features, followed by AVOD [75], which uses FPN to extract high-resolution features for feature fusion; methods such as Pointfusion [81], MVX-Net [82], and Pointpainting [83] have further explored the benefits of different fusion granularity feature fusion advantages.”
Li, Z., Wang, W., Li, H., Xie, E., Sima, C., Lu, T., Yu, Q., & Dai, J. (2022) · arXiv · doi:10.48550/arxiv.2203.17270 · Cited by 1,419 · see citing works
“580 - - - - PointPainting [43] L&C - 0.”
MethodInfluentialGAFusion: Adaptive Fusing LiDAR and Camera with Multiple Guidance for 3D Object Detection
Li, X., Fan, B., Tian, J., & Fan, H. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.02004 · Cited by 42 · see citing works
“PointPainting [36] enriches point clouds with semantic labels from images.”
BackgroundPan, C., He, Y., Peng, J., Zhang, Q., Sui, W., & Zhang, Z. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00925 · Cited by 33 · see citing works
Multi-Sem Fusion: Multimodal Semantic Fusion for 3-D Object Detection
Xu, S., Li, F., Song, Z., Fang, J., Wang, S., & Yang, Z.-X. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3387732 · Cited by 26 · see citing works
Exploring Sensor Impact and Architectural Robustness in Adverse Weather on BEV Perception
Kumar, S., Sharma, S., Asghar, R., Mohandas, R., Brophy, T., Sistu, G., Grua, E. M., Donzella, V., & Eising, C. (2025) · IEEE Open Journal of Vehicular Technology · doi:10.1109/ojvt.2025.3621862 · Cited by 5 · see citing works
- Authors
- Vora, S., Lang, A. H., Helou, B., & Beijbom, O.
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 4603-4611
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2989996808
- Cited by
- 1,236
- References
- 33
- Citations by year
- 2026: 190 · 2025: 271 · 2024: 295 · 2023: 237 · 2022: 139 · 2021: 82 · 2020: 22
- FWCI
- 37.02
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- 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 › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Keywords
- 3D object detection, sensor fusion, LiDAR-camera fusion, point cloud semantic segmentation, sequential fusion, PointPillars, autonomous vehicles, KITTI, nuScenes, pipeline latency
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
