Skip to main content
Springer Nature Link
Log in

Better Call SAL: Towards Learning to Segment Anything in Lidar

  • Conference paper
  • First Online:
Computer Vision – ECCV 2024 (ECCV 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15097))

Included in the following conference series:

  • 507 Accesses

Abstract

We propose the SAL (Segment Anything in Lidar) method consisting of a text-promptable zero-shot model for segmenting and classifying any object in Lidar, and a pseudo-labeling engine that facilitates model training without manual supervision. While the established paradigm for Lidar Panoptic Segmentation (LPS) relies on manual supervision for a handful of object classes defined a priori, we utilize 2D vision foundation models to generate 3D supervision “for free”. Our pseudo-labels consist of instance masks and corresponding CLIP tokens, which we lift to Lidar using calibrated multi-modal data. By training our model on these labels, we distill the 2D foundation models into our Lidar SAL model. Even without manual labels, our model achieves \(91\%\) in terms of class-agnostic segmentation and \(54\%\) in terms of zero-shot LPS of the fully supervised state-of-the-art. Furthermore, we outperform several baselines that do not distill but only lift image features to 3D. More importantly, we demonstrate that SAL supports arbitrary class prompts, can be easily extended to new datasets, and shows significant potential to improve with increasing amounts of self-labeled data. We release all models and the code.

A. Ošep and T. Meinhardt—Equal contribution.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S.: Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)

    Article  Google Scholar 

  2. Agarwalla, A., et al.: Lidar panoptic segmentation and tracking without bells and whistles. In: International Conference on Intelligent Robots and Systems (2023)

    Google Scholar 

  3. Aksoy, E.E., Baci, S., Cavdar, S.: SalsaNet: fast road and vehicle segmentation in lidar point clouds for autonomous driving. In: Intelligent Vehicles Symposium (2020)

    Google Scholar 

  4. Aygün, M., et al.: 4D panoptic lidar segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (2021)

    Google Scholar 

  5. Bansal, A., Sikka, K., Sharma, G., Chellappa, R., Divakaran, A.: Zero-shot object detection. In: European Conference on Computer Vision (2018)

    Google Scholar 

  6. Behley, J., et al.: SemanticKITTI: a dataset for semantic scene understanding of LiDAR sequences. In: International Conference on Computer Vision (2019)

    Google Scholar 

  7. Behley, J., Milioto, A., Stachniss, C.: A benchmark for LiDAR-based panoptic segmentation based on KITTI. In: International Conference on Robotics and Automation (2021)

    Google Scholar 

  8. Bucher, M., Vu, T.H., Cord, M., Pérez, P.: Zero-shot semantic segmentation. In: Advances in Neural Information Processing Systems (2019)

    Google Scholar 

  9. Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: European Conference on Computer Vision (2020)

    Google Scholar 

  10. Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. In: Advances in Neural Information Processing Systems (2020)

    Google Scholar 

  11. Cheng, B., Misra, I., Schwing, A.G., Kirillov, A., Girdhar, R.: Masked-attention mask transformer for universal image segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (2022)

    Google Scholar 

  12. Choy, C., Gwak, J., Savarese, S.: 4D spatio-temporal convnets: minkowski convolutional neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition (2019)

    Google Scholar 

  13. Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. In: IEEE Conference on Computer Vision and Pattern Recognition (2016)

    Google Scholar 

  14. Dai, A., Chang, A.X., Savva, M., Halber, M., Funkhouser, T., Nießner, M.: ScanNet: richly-annotated 3D reconstructions of indoor scenes. In: IEEE Conference on Computer Vision and Pattern Recognition (2017)

    Google Scholar 

  15. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition (2009)

    Google Scholar 

  16. Ding, Z., Wang, J., Tu, Z.: Open-vocabulary universal image segmentation with maskclip. In: International Conference on Machine Learning (2023)

    Google Scholar 

  17. Ester, M., Kriegel, H.P., Sander, J., Xu, X., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Robotics: Science and Systems (1996)

    Google Scholar 

  18. Fong, W.K., et al.: Panoptic nuScenes: a large-scale benchmark for lidar panoptic segmentation and tracking. IEEE Robot. Autom. Lett. 7, 3795–3802 (2021)

    Article  Google Scholar 

  19. Gasperini, S., Mahani, M.A.N., Marcos-Ramiro, A., Navab, N., Tombari, F.: Panoster: end-to-end panoptic segmentation of lidar point clouds. IEEE Robot. Autom. Lett. 6, 3216–3223 (2021)

    Article  Google Scholar 

  20. Ghiasi, G., Gu, X., Cui, Y., Lin, T.Y.: Scaling open-vocabulary image segmentation with image-level labels. In: European Conference on Computer Vision (2022)

    Google Scholar 

  21. Gu, X., Lin, T.Y., Kuo, W., Cui, Y.: Open-vocabulary object detection via vision and language knowledge distillation. arXiv preprint arXiv:2104.13921 (2021)

  22. Harley, A.W., et al.: Track, check, repeat: an EM approach to unsupervised tracking. In: IEEE Conference on Computer Vision and Pattern Recognition (2021)

    Google Scholar 

  23. He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: IEEE Conference on Computer Vision and Pattern Recognition (2020)

    Google Scholar 

  24. Held, D., Guillory, D., Rebsamen, B., Thrun, S., Savarese, S.: A probabilistic framework for real-time 3D segmentation using spatial, temporal, and semantic cues. In: Robotics: Science and Systems (2016)

    Google Scholar 

  25. Held, D., Levinson, J., Thrun, S., Savarese, S.: Combining 3D shape, color, and motion for robust anytime tracking. In: Robotics: Science and Systems (2014)

    Google Scholar 

  26. Hong, F., Zhou, H., Zhu, X., Li, H., Liu, Z.: Lidar-based panoptic segmentation via dynamic shifting network. In: IEEE Conference on Computer Vision and Pattern Recognition (2021)

    Google Scholar 

  27. Hu, P., Held, D., Ramanan, D.: Learning to optimally segment point clouds. IEEE Robot. Autom. Lett. 5(2), 875–882 (2020)

    Article  Google Scholar 

  28. Hurtado, J.V., Mohan, R., Valada, A.: MOPT: multi-object panoptic tracking. arXiv preprint arXiv:2004.08189 (2020)

  29. Kirillov, A., He, K., Girshick, R.B., Rother, C., Dollár, P.: Panoptic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (2018)

    Google Scholar 

  30. Kirillov, A., et al.: Segment anything. In: International Conference on Computer Vision (2023)

    Google Scholar 

  31. Kreuzberg, L., Zulfikar, I.E., Mahadevan, S., Engelmann, F., Leibe, B.: 4D-stop: panoptic segmentation of 4D lidar using spatio-temporal object proposal generation and aggregation. In: ECCV AVVision Workshop (2022)

    Google Scholar 

  32. Lang, A.H., Vora, S., Caesar, H., Zhou, L., Yang, J., Beijbom, O.: PointPillars: fast encoders for object detection from point clouds. In: IEEE Conference on Computer Vision and Pattern Recognition (2019)

    Google Scholar 

  33. Li, B., Weinberger, K.Q., Belongie, S., Koltun, V., Ranftl, R.: Language-driven semantic segmentation. In: International Conference on Learning Representations (2022)

    Google Scholar 

  34. Li, J., He, X., Wen, Y., Gao, Y., Cheng, Y., Zhang, D.: Panoptic-PHNet: towards real-time and high-precision lidar panoptic segmentation via clustering pseudo heatmap. In: IEEE Conference on Computer Vision and Pattern Recognition (2022)

    Google Scholar 

  35. Li, S., Chen, X., Liu, Y., Dai, D., Stachniss, C., Gall, J.: Multi-scale interaction for real-time lidar data segmentation on an embedded platform. IEEE Robot. Autom. Lett. 7(2), 738–745 (2021)

    Article  Google Scholar 

  36. Liang, F., et al.: Open-vocabulary semantic segmentation with mask-adapted clip. In: IEEE Conference on Computer Vision and Pattern Recognition (2023)

    Google Scholar 

  37. Lin, T., et al.: Microsoft COCO: common objects in context. In: European Conference on Computer Vision (2014)

    Google Scholar 

  38. Lin, Z., Pathak, D., Wang, Y.X., Ramanan, D., Kong, S.: Continual learning with evolving class ontologies. In: Advances in Neural Information Processing Systems (2022)

    Google Scholar 

  39. Liu, Y., et al.: Segment any point cloud sequences by distilling vision foundation models. arXiv preprint arXiv:2306.09347 (2023)

  40. Liu, Z., Zhang, Z., Cao, Y., Hu, H., Tong, X.: Group-free 3D object detection via transformers. In: International Conference on Computer Vision (2021)

    Google Scholar 

  41. Lu, Y., Jiang, Q., Chen, R., Hou, Y., Zhu, X., Ma, Y.: See more and know more: zero-shot point cloud segmentation via multi-modal visual data. In: International Conference on Computer Vision (2023)

    Google Scholar 

  42. Ma, Y., et al.: Long-tailed 3D detection via 2D late fusion. arXiv preprint arXiv:2312.10986 (2023)

  43. Marcuzzi, R., Nunes, L., Wiesmann, L., Behley, J., Stachniss, C.: Mask-based panoptic lidar segmentation for autonomous driving. IEEE Robot. Autom. Lett. 8(2), 1141–1148 (2023)

    Article  Google Scholar 

  44. Marcuzzi, R., Nunes, L., Wiesmann, L., Marks, E., Behley, J., Stachniss, C.: Mask4D: end-to-end mask-based 4D panoptic segmentation for lidar sequences. IEEE Robot. Autom. Lett. (2023)

    Google Scholar 

  45. Marcuzzi, R., Nunes, L., Wiesmann, L., Vizzo, I., Behley, J., Stachniss, C.: Contrastive instance association for 4D panoptic segmentation using sequences of 3D lidar scans. IEEE Robot. Autom. Lett. (2022)

    Google Scholar 

  46. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)

  47. Milioto, A., Vizzo, I., Behley, J., Stachniss, C.: RangeNet++: fast and accurate LiDAR semantic segmentation. In: International Conference on Intelligent Robots and Systems (2019)

    Google Scholar 

  48. Miller, D., Nicholson, L., Dayoub, F., Sünderhauf, N.: Dropout sampling for robust object detection in open-set conditions. In: International Conference on Robotics and Automation (2018)

    Google Scholar 

  49. Moosmann, F., Stiller, C.: Joint self-localization and tracking of generic objects in 3D range data. In: International Conference on Robotics and Automation (2013)

    Google Scholar 

  50. Najibi, M., et al.: Motion inspired unsupervised perception and prediction in autonomous driving. In: European Conference on Computer Vision (2022)

    Google Scholar 

  51. Najibi, M., et al.: Unsupervised 3D perception with 2D vision-language distillation for autonomous driving. In: International Conference on Computer Vision (2023)

    Google Scholar 

  52. Nunes, L., Marcuzzi, R., Chen, X., Behley, J., Stachniss, C.: SegContrast: 3D point cloud feature representation learning through self-supervised segment discrimination. IEEE Robot. Autom. Lett. 7(2), 2116–2123 (2022)

    Article  Google Scholar 

  53. Osep, A., Voigtlaender, P., Luiten, J., Breuers, S., Leibe, B.: Towards large-scale video video object mining. In: ECCV Workshop on Interactive and Adaptive Learning in an Open World (2018)

    Google Scholar 

  54. Ošep, A., Mehner, W., Voigtlaender, P., Leibe, B.: Track, then decide: category-agnostic vision-based multi-object tracking. In: International Conference on Robotics and Automation (2018)

    Google Scholar 

  55. Ošep, A., Voigtlaender, P., Luiten, J., Breuers, S., Leibe, B.: Large-scale object mining for object discovery from unlabeled video. In: International Conference on Robotics and Automation (2019)

    Google Scholar 

  56. Peng, S., Genova, K., Jiang, C., Tagliasacchi, A., Pollefeys, M., Funkhouser, T.: OpenScene: 3D scene understanding with open vocabularies. In: IEEE Conference on Computer Vision and Pattern Recognition (2023)

    Google Scholar 

  57. Peri, N., Dave, A., Ramanan, D., Kong, S.: Towards long-tailed 3D detection. In: Conference on Robot Learning (2023)

    Google Scholar 

  58. Peri, N., Li, M., Wilson, B., Wang, Y.X., Hays, J., Ramanan, D.: An empirical analysis of range for 3D object detection. In: ICCV Workshops (2023)

    Google Scholar 

  59. Peri, N., Luiten, J., Li, M., Ošep, A., Leal-Taixé, L., Ramanan, D.: Forecasting from lidar via future object detection. In: IEEE Conference on Computer Vision and Pattern Recognition (2022)

    Google Scholar 

  60. Petrovskaya, A., Thrun, S.: Model based vehicle detection and tracking for autonomous urban driving. Auton. Rob. 26, 123–139 (2009)

    Article  Google Scholar 

  61. Pot, E., Toshev, A., Kosecka, J.: Self-supervisory signals for object discovery and detection. arXiv preprint arXiv:1806.03370 (2018)

  62. Prest, A., Leistner, C., Civera, J., Schmid, C., Ferrari, V.: Learning object class detectors from weakly annotated video. In: IEEE Conference on Computer Vision and Pattern Recognition (2012)

    Google Scholar 

  63. Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (2017)

    Google Scholar 

  64. Qi, C.R., Yi, L., Su, H., Guibas, L.J.: PointNet++: deep hierarchical feature learning on point sets in a metric space. In: Advances in Neural Information Processing Systems (2017)

    Google Scholar 

  65. Radford, A., et al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning (2021)

    Google Scholar 

  66. Rahman, S., Khan, S.H., Porikli, F.: Zero-shot object detection: learning to simultaneously recognize and localize novel concepts. In: Asian Conference on Computer Vision (2018)

    Google Scholar 

  67. Rao, Y., et al.: DenseCLIP: language-guided dense prediction with context-aware prompting. In: IEEE Conference on Computer Vision and Pattern Recognition (2022)

    Google Scholar 

  68. Razani, R., Cheng, R., Li, E., Taghavi, E., Ren, Y., Bingbing, L.: GP-S3Net: graph-based panoptic sparse semantic segmentation network. In: IEEE Conference on Computer Vision and Pattern Recognition (2021)

    Google Scholar 

  69. Razani, R., Cheng, R., Taghavi, E., Bingbing, L.: Lite-HDSeg: lidar semantic segmentation using lite harmonic dense convolutions. In: International Conference on Robotics and Automation (2021)

    Google Scholar 

  70. Sautier, C., Puy, G., Gidaris, S., Boulch, A., Bursuc, A., Marlet, R.: Image-to-lidar self-supervised distillation for autonomous driving data. In: IEEE Conference on Computer Vision and Pattern Recognition (2022)

    Google Scholar 

  71. Seidenschwarz, J., Ošep, A., Ferroni, F., Lucey, S., Leal-Taixé, L.: SeMoLi: what moves together belongs together. In: IEEE Conference on Computer Vision and Pattern Recognition (2024)

    Google Scholar 

  72. Sirohi, K., Mohan, R., Büscher, D., Burgard, W., Valada, A.: EfficientLPS: efficient lidar panoptic segmentation. IEEE Trans. Robot. (2021)

    Google Scholar 

  73. Sun, P., et al.: Scalability in perception for autonomous driving: waymo open dataset. In: IEEE Conference on Computer Vision and Pattern Recognition (2020)

    Google Scholar 

  74. Takmaz, A., Fedele, E., Sumner, R.W., Pollefeys, M., Tombari, F., Engelmann, F.: Openmask3D: open-vocabulary 3D instance segmentation. arXiv preprint arXiv:2306.13631 (2023)

  75. Tang, H., et al.: Searching efficient 3D architectures with sparse point-voxel convolution. In: European Conference on Computer Vision (2020)

    Google Scholar 

  76. Teichman, A., Levinson, J., Thrun, S.: Towards 3D object recognition via classification of arbitrary object tracks. In: International Conference on Robotics and Automation (2011)

    Google Scholar 

  77. Thomas, H., Qi, C.R., Deschaud, J.E., Marcotegui, B., Goulette, F., Guibas, L.J.: KPConv: flexible and deformable convolution for point clouds. In: International Conference on Computer Vision (2019)

    Google Scholar 

  78. Thorpe, C., Herbert, M., Kanade, T., Shafer, S.: Toward autonomous driving: the CMU Navlab. I. perception. IEEE Expert 6(4), 31–42 (1991)

    Article  Google Scholar 

  79. Thrun, S., et al.: Stanley: the robot that won the DARPA grand challenge. J. Field Robot. (2006)

    Google Scholar 

  80. Wang, Y., et al.: Train in Germany, test in the USA: making 3D object detectors generalize. In: IEEE Conference on Computer Vision and Pattern Recognition (2020)

    Google Scholar 

  81. Wong, K., Wang, S., Ren, M., Liang, M., Urtasun, R.: Identifying unknown instances for autonomous driving. In: Conference on Robot Learning, pp. 384–393. PMLR (2020)

    Google Scholar 

  82. Wu, B., Wan, A., Yue, X., Keutzer, K.: SqueezeSeg: convolutional neural nets with recurrent CRF for real-time road-object segmentation from 3D lidar point cloud. In: International Conference on Robotics and Automation (2018)

    Google Scholar 

  83. Wu, B., Zhou, X., Zhao, S., Yue, X., Keutzer, K.: SqueezeSegV2: improved model structure and unsupervised domain adaptation for road-object segmentation from a lidar point cloud. In: International Conference on Robotics and Automation (2019)

    Google Scholar 

  84. Xian, Y., Lampert, C.H., Schiele, B., Akata, Z.: Zero-shot learning - a comprehensive evaluation of the good, the bad and the ugly. IEEE Trans. Pattern Anal. Mach. Intell. 41, 2251–2265 (2018)

    Article  Google Scholar 

  85. Xiong, X., Munoz, D., Bagnell, J.A., Hebert, M.: 3-D scene analysis via sequenced predictions over points and regions. In: International Conference on Robotics and Automation, pp. 2609–2616 (2011)

    Google Scholar 

  86. Xu, J., Liu, S., Vahdat, A., Byeon, W., Wang, X., De Mello, S.: Open-vocabulary panoptic segmentation with text-to-image diffusion models. In: IEEE Conference on Computer Vision and Pattern Recognition (2023)

    Google Scholar 

  87. Xu, M., Zhang, Z., Wei, F., Hu, H., Bai, X.: Side adapter network for open-vocabulary semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (2023)

    Google Scholar 

  88. Yan, Y., Mao, Y., Li, B.: Second: sparsely embedded convolutional detection. Sensors 18(10), 3337 (2018)

    Article  Google Scholar 

  89. Yilmaz, K., Schult, J., Nekrasov, A., Leibe, B.: Mask4D: mask transformer for 4D panoptic segmentation. arXiv preprint arXiv:2309.16133 (2023)

  90. Yin, T., Zhou, X., Krähenbühl, P.: Center-based 3D object detection and tracking. In: IEEE Conference on Computer Vision and Pattern Recognition (2021)

    Google Scholar 

  91. Zareian, A., Rosa, K.D., Hu, D.H., Chang, S.F.: Open-vocabulary object detection using captions. In: IEEE Conference on Computer Vision and Pattern Recognition (2021)

    Google Scholar 

  92. Zhang, L., et al.: Towards unsupervised object detection from lidar point clouds. In: IEEE Conference on Computer Vision and Pattern Recognition (2023)

    Google Scholar 

  93. Zhong, Y., et al.: RegionCLIP: region-based language-image pretraining. In: IEEE Conference on Computer Vision and Pattern Recognition (2022)

    Google Scholar 

  94. Zhou, C., Loy, C.C., Dai, B.: Extract free dense labels from clip. In: European Conference on Computer Vision (2022)

    Google Scholar 

  95. Zhou, Y., Tuzel, O.: VoxelNet: end-to-end learning for point cloud based 3D object detection. In: IEEE Conference on Computer Vision and Pattern Recognition (2018)

    Google Scholar 

  96. Zhou, Z., Zhang, Y., Foroosh, H.: Panoptic-polarnet: proposal-free lidar point cloud panoptic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (2021)

    Google Scholar 

  97. Zhu, M., Han, S., Cai, H., Borse, S., Ghaffari, M., Porikli, F.: 4D panoptic segmentation as invariant and equivariant field prediction. In: IEEE Conference on Computer Vision and Pattern Recognition (2023)

    Google Scholar 

  98. Zhu, X., et al.: Cylindrical and asymmetrical 3D convolution networks for lidar segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (2021)

    Google Scholar 

Download references

Acknowledgments

This project was funded, in parts, by ERC Starting Grant DynAI (ERC-101043189). We are grateful to Žan Gojčič, Guillem Braso, Cristiano Saltori, Sérgio Agostinho, and Jonas Schult for their feedback on the paper and their insightful comments. Special thanks to Maxim Maximov for his help on figures.

Author information

Authors and Affiliations

  1. NVIDIA, Santa Clara, USA

    Aljoša Ošep, Tim Meinhardt, Francesco Ferroni & Laura Leal-Taixé

  2. CMU, Pittsburgh, USA

    Neehar Peri & Deva Ramanan

Authors
  1. Aljoša Ošep
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tim Meinhardt
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Francesco Ferroni
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Neehar Peri
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Deva Ramanan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Laura Leal-Taixé
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aljoša Ošep .

Editor information

Editors and Affiliations

  1. University of Birmingham, Birmingham, UK

    Aleš Leonardis

  2. University of Trento, Trento, Italy

    Elisa Ricci

  3. Technical University of Darmstadt, Darmstadt, Hessen, Germany

    Stefan Roth

  4. Princeton University, Palo Alto, CA, USA

    Olga Russakovsky

  5. Czech Technical University in Prague, Prague, Czech Republic

    Torsten Sattler

  6. École des Ponts ParisTech, Marne-la-Vallée, France

    Gül Varol

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 41530 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ošep, A., Meinhardt, T., Ferroni, F., Peri, N., Ramanan, D., Leal-Taixé, L. (2025). Better Call SAL: Towards Learning to Segment Anything in Lidar. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15097. Springer, Cham. https://doi.org/10.1007/978-3-031-72933-1_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-72933-1_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-72932-4

  • Online ISBN: 978-3-031-72933-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics