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The complex traffic environment and various weather conditions make the collection of LiDAR data expensive and challenging.
Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? the kitti vision benchmark suite. In: 2012 IEEE conference on computer vision and pattern recognition. pp. 3354–3361. IEEE (2012)
2012
Earlier work this paper cites.
Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)
2013
Earlier work this paper cites.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. Advances in neural information processing systems 27
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13. pp. 740–755. Springer (2014)
2014
Earlier work this paper cites.
2015
Earlier work this paper cites.
Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18. pp. 234–241. Springer (2015)
2015
Earlier work this paper cites.
Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on robot learning. pp. 1–16. PMLR (2017)
2017
Earlier work this paper cites.
Qi, C.R., Su, H., Mo, K., Guibas, L.J.: Pointnet: Deep learning on point sets for 3d classification and segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 652–660 (2017)
2017
Earlier work this paper cites.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30
2017
Earlier work this paper cites.
Achlioptas, P., Diamanti, O., Mitliagkas, I., Guibas, L.: Learning representations and generative models for 3d point clouds. In: International conference on machine learning. pp. 40–49. PMLR (2018)
2018
Earlier work this paper cites.
Valsesia, D., Fracastoro, G., Magli, E.: Learning localized generative models for 3d point clouds via graph convolution. In: International conference on learning representations (2018)
2018
Earlier work this paper cites.
Behley, J., Garbade, M., Milioto, A., Quenzel, J., Behnke, S., Stachniss, C., Gall, J.: SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences. In: Proc. of the IEEE/CVF International Conf. on Computer Vision (ICCV) (2019)
2019
Earlier work this paper cites.
Caccia, L., Van Hoof, H., Courville, A., Pineau, J.: Deep generative modeling of lidar data. In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). pp. 5034–5040. IEEE (2019)
2019
Earlier work this paper cites.
Chen, K., Choy, C.B., Savva, M., Chang, A.X., Funkhouser, T., Savarese, S.: Text2shape: Generating shapes from natural language by learning joint embeddings. In: Computer Vision–ACCV 2018: 14th Asian Conference on Computer Vision, Perth, Australia, December 2–6, 2018, Revised Selected Papers, Part III 14. pp. 100–116. Springer (2019)
2019
Earlier work this paper cites.
Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 5356–5364 (2019)
2019
Earlier work this paper cites.
Meyer, G.P., Laddha, A., Kee, E., Vallespi-Gonzalez, C., Wellington, C.K.: Lasernet: An efficient probabilistic 3d object detector for autonomous driving. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 12677–12686 (2019)
2019
Earlier work this paper cites.
Milioto, A., Vizzo, I., Behley, J., Stachniss, C.: Rangenet++: Fast and accurate lidar semantic segmentation. In: 2019 IEEE/RSJ international conference on intelligent robots and systems (IROS). pp. 4213–4220. IEEE (2019)
2019
Earlier work this paper cites.
Milioto, A., Vizzo, I., Behley, J., Stachniss, C.: Rangenet++: Fast and accurate lidar semantic segmentation. In: 2019 IEEE/RSJ international conference on intelligent robots and systems (IROS). pp. 4213–4220. IEEE (2019)
2019
Earlier work this paper cites.
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems 32
2019
Earlier work this paper cites.
Schubert, S., Neubert, P., Pöschmann, J., Protzel, P.: Circular convolutional neural networks for panoramic images and laser data. In: 2019 IEEE intelligent vehicles symposium (IV). pp. 653–660. IEEE (2019)
2019
Earlier work this paper cites.
Song, Y., Ermon, S.: Generative modeling by estimating gradients of the data distribution. Advances in neural information processing systems 32
2019
Earlier work this paper cites.
Yang, G., Huang, X., Hao, Z., Liu, M.Y., Belongie, S., Hariharan, B.: Pointflow: 3d point cloud generation with continuous normalizing flows. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 4541–4550 (2019)
2019
Earlier work this paper cites.
Bakhshi, R., Sandborn, P.: Maximizing the returns of lidar systems in wind farms for yaw error correction applications. Wind Energy 23
2020
Earlier work this paper cites.
Caesar, H., Bankiti, V., Lang, A.H., Vora, S., Liong, V.E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., Beijbom, O.: nuscenes: A multimodal dataset for autonomous driving. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 11621–11631 (2020)
2020
Earlier work this paper cites.
Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Advances in neural information processing systems 33
2020
Earlier work this paper cites.
Hui, L., Xu, R., Xie, J., Qian, J., Yang, J.: Progressive point cloud deconvolution generation network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XV 16. pp. 397–413. Springer (2020)
2020
Earlier work this paper cites.
Janai, J., Güney, F., Behl, A., Geiger, A., et al.: Computer vision for autonomous vehicles: Problems, datasets and state of the art. Foundations and Trends® in Computer Graphics and Vision 12
2020
Earlier work this paper cites.
Klokov, R., Boyer, E., Verbeek, J.: Discrete point flow networks for efficient point cloud generation. In: European Conference on Computer Vision. pp. 694–710. Springer (2020)
2020
Earlier work this paper cites.
Manivasagam, S., Wang, S., Wong, K., Zeng, W., Sazanovich, M., Tan, S., Yang, B., Ma, W.C., Urtasun, R.: Lidarsim: Realistic lidar simulation by leveraging the real world. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 11167–11176 (2020)
2020
Earlier work this paper cites.
Tancik, M., Srinivasan, P., Mildenhall, B., Fridovich-Keil, S., Raghavan, N., Singhal, U., Ramamoorthi, R., Barron, J., Ng, R.: Fourier features let networks learn high frequency functions in low dimensional domains. Advances in Neural Information Processing Systems 33
2020
Cited alongside, same era.
Zamorski, M., Zięba, M., Klukowski, P., Nowak, R., Kurach, K., Stokowiec, W., Trzciński, T.: Adversarial autoencoders for compact representations of 3d point clouds. Computer Vision and Image Understanding 193
2020
Cited alongside, same era.
Chai, Y., Sun, P., Ngiam, J., Wang, W., Caine, B., Vasudevan, V., Zhang, X., Anguelov, D.: To the point: Efficient 3d object detection in the range image with graph convolution kernels. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 16000–16009 (2021)
2021
Cited alongside, same era.
Deliry, S.I., Avdan, U.: Accuracy of unmanned aerial systems photogrammetry and structure from motion in surveying and mapping: a review. Journal of the Indian Society of Remote Sensing 49
2023
Later among the works it cites.
2023
Later among the works it cites.
Cho, J., Zala, A., Bansal, M.: Dall-eval: Probing the reasoning skills and social biases of text-to-image generation models. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 3043–3054 (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
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2021
Cited alongside, same era.
Fu, M., Liu, H., Yu, Y., Chen, J., Wang, K.: Dw-gan: A discrete wavelet transform gan for nonhomogeneous dehazing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 203–212 (2021)
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Luo, S., Hu, W.: Diffusion probabilistic models for 3d point cloud generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 2837–2845 (2021)
2021
Cited alongside, same era.
Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM 65
2021
Cited alongside, same era.
Nakashima, K., Kurazume, R.: Learning to drop points for lidar scan synthesis. In: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). pp. 222–229. IEEE (2021)
2021
Cited alongside, same era.
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: International conference on machine learning. pp. 8748–8763. PMLR (2021)
2021
Cited alongside, same era.
Sauer, A., Chitta, K., Müller, J., Geiger, A.: Projected gans converge faster. Advances in Neural Information Processing Systems 34
2021
Cited alongside, same era.
Wen, C., Yu, B., Tao, D.: Learning progressive point embeddings for 3d point cloud generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 10266–10275 (2021)
2021
Cited alongside, same era.
Ge, S., Park, T., Zhu, J.Y., Huang, J.B.: Expressive text-to-image generation with rich text. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 7545–7556 (2023)
2023
Later among the works it cites.
Kerbl, B., Kopanas, G., Leimkühler, T., Drettakis, G.: 3d gaussian splatting for real-time radiance field rendering. ACM Transactions on Graphics 42
2023
Later among the works it cites.
Kim, Y., Lee, J., Kim, J.H., Ha, J.W., Zhu, J.Y.: Dense text-to-image generation with attention modulation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 7701–7711 (2023)
2023
Later among the works it cites.
Kong, L., Liu, Y., Li, X., Chen, R., Zhang, W., Ren, J., Pan, L., Chen, K., Liu, Z.: Robo3d: Towards robust and reliable 3d perception against corruptions. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 19994–20006 (2023)
2023
Later among the works it cites.
Li, Z., Li, X., Yang, L., Zhao, B., Song, R., Luo, L., Li, J., Yang, J.: Curriculum temperature for knowledge distillation. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 37, pp. 1504–1512 (2023)
2023
Later among the works it cites.
Mohsan, S.A.H., Othman, N.Q.H., Li, Y., Alsharif, M.H., Khan, M.A.: Unmanned aerial vehicles (uavs): Practical aspects, applications, open challenges, security issues, and future trends. Intelligent Service Robotics 16
2023
Later among the works it cites.
Nakashima, K., Iwashita, Y., Kurazume, R.: Generative range imaging for learning scene priors of 3d lidar data. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 1256–1266 (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
Piroli, A., Dallabetta, V., Kopp, J., Walessa, M., Meissner, D., Dietmayer, K.: Energy-based detection of adverse weather effects in lidar data. IEEE Robotics and Automation Letters (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
Wang, Y., Mao, Q., Zhu, H., Deng, J., Zhang, Y., Ji, J., Li, H., Zhang, Y.: Multi-modal 3d object detection in autonomous driving: a survey. International Journal of Computer Vision pp. 1–31 (2023)
2023
Later among the works it cites.
Wu, J.Z., Ge, Y., Wang, X., Lei, S.W., Gu, Y., Shi, Y., Hsu, W., Shan, Y., Qie, X., Shou, M.Z.: Tune-a-video: One-shot tuning of image diffusion models for text-to-video generation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 7623–7633 (2023)
2023
Later among the works it cites.
Wu, L., Wang, D., Gong, C., Liu, X., Xiong, Y., Ranjan, R., Krishnamoorthi, R., Chandra, V., Liu, Q.: Fast point cloud generation with straight flows. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9445–9454 (2023)
2023
Later among the works it cites.
Wu, Z., Wang, Y., Feng, M., Xie, H., Mian, A.: Sketch and text guided diffusion model for colored point cloud generation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 8929–8939 (2023)
2023
Later among the works it cites.
Yang, X., Zhou, D., Feng, J., Wang, X.: Diffusion probabilistic model made slim. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 22552–22562 (2023)
2023
Later among the works it cites.
Yin, H., Lin, Z., Yeoh, J.K.: Semantic localization on bim-generated maps using a 3d lidar sensor. Automation in Construction 146
2023
Later among the works it cites.
Cui, C., Ma, Y., Cao, X., Ye, W., Zhou, Y., Liang, K., Chen, J., Lu, J., Yang, Z., Liao, K.D., et al.: A survey on multimodal large language models for autonomous driving. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 958–979 (2024)
2024
Closest in time.
Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. Advances in Neural Information Processing Systems 36
2024
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Kasten, Y., Rahamim, O., Chechik, G.: Point cloud completion with pretrained text-to-image diffusion models. Advances in Neural Information Processing Systems 36
2024
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Li, Z., Li, X., Fu, X., Zhang, X., Wang, W., Chen, S., Yang, J.: Promptkd: Unsupervised prompt distillation for vision-language models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 26617–26626 (2024)
2024
Closest in time.
Xu, J., Liu, X., Wu, Y., Tong, Y., Li, Q., Ding, M., Tang, J., Dong, Y.: Imagereward: Learning and evaluating human preferences for text-to-image generation. Advances in Neural Information Processing Systems 36
2024
Closest in time.
Xu, Z., Xing, S., Sangineto, E., Sebe, N.: Spectralclip: Preventing artifacts in text-guided style transfer from a spectral perspective. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 5121–5130 (2024)
2024
Closest in time.
2024
Closest in time.
Yan, Z., Lin, Y., Wang, K., Zheng, Y., Wang, Y., Zhang, Z., Li, J., Yang, J.: Tri-perspective view decomposition for geometry-aware depth completion. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 4874–4884 (2024)
2024
Closest in time.