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Semantic scene understanding is crucial for robotics and computer vision applications.
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J. Behley, M. Garbade, A. Milioto, J. Quenzel, S. Behnke, C. Stachniss, and J. Gall, “SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences,” in Proc. of the IEEE/CVF Intl. Conf. on Computer Vision (ICCV) , 2019
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B. Wu, X. Zhou, S. Zhao, X. Yue, and K. Keutzer, “SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud,” in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA) , 2019
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J. Behley, M. Garbade, A. Milioto, J. Quenzel, S. Behnke, C. Stachniss, and J. Gall, “SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences,” in
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T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila, “Analyzing and Improving the Image Quality of StyleGAN,” in Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR) , 2020
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P. Sun, H. Kretzschmar, X. Dotiwalla, A. Chouard, V. Patnaik, P. Tsui, J. Guo, Y. Zhou, Y. Chai, B. Caine, V. Vasudevan, W. Han, J. Ngiam, H. Zhao, A. Timofeev, S. Ettinger, M. Krivokon, A. Gao, A. Joshi, Y. Zhang, J. Shlens, Z. Chen, and D. Anguelov, “Scalability in Perception for Autonomous Driving: Waymo Open Dataset,” in Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR) , 2020
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H. Tang, Z. Liu, S. Zhao, Y. Lin, J. Lin, H. Wang, and S. Han, “Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution,” in Proc. of the Europ. Conf. on Computer Vision (ECCV) , 2020
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V. Zyrianov, X. Zhu, and S. Wang, “Learning to generate realistic lidar point clouds,” in Proc. of the Europ. Conf. on Computer Vision (ECCV) , 2022
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N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, “End-to-end object detection with transformers,” in
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P. Sun, H. Kretzschmar, X. Dotiwalla, A. Chouard, V. Patnaik, P. Tsui, J. Guo, Y. Zhou, Y. Chai, B. Caine, V. Vasudevan, W. Han, J. Ngiam, H. Zhao, A. Timofeev, S. Ettinger, M. Krivokon, A. Gao, A. Joshi, Y. Zhang, J. Shlens, Z. Chen, and D. Anguelov, “Scalability in Perception for Autonomous Driving: Waymo Open Dataset,” in
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M. Caron, H. Touvron, I. Misra, H. Jégou, J. Mairal, P. Bojanowski, and A. Joulin, “Emerging properties in self-supervised vision transformers,” in Proc. of the IEEE/CVF Intl. Conf. on Computer Vision (ICCV) , 2021
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P. Dhariwal and A. Nichol, “Diffusion models beat gans on image synthesis,” in Proc. of the Conf. on Neural Information Processing Systems (NeurIPS) , 2021
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Y. Tian, L. Fan, P. Isola, H. Chang, and D. Krishnan, “Stablerep: Synthetic images from text-to-image models make strong visual representation learners,” in Proc. of the Conf. on Neural Information Processing Systems (NeurIPS) , 2021
2021
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L. Zhou, Y. Du, and J. Wu, “3D Shape Generation and Completion Through Point-Voxel Diffusion,” in Proc. of the IEEE/CVF Intl. Conf. on Computer Vision (ICCV) , 2021
2021
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X. Zhu, H. Zhou, T. Wang, F. Hong, Y. Ma, W. Li, H. Li, and D. Lin, “Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation,” in Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR) , 2021
2021
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A. Blattmann, R. Rombach, K. Oktay, J. Müller, and B. Ommer, “Retrieval-augmented diffusion models,” in Proc. of the Conf. on Neural Information Processing Systems (NeurIPS) , 2022
2022
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W. Peebles and S. Xie, “Scalable diffusion models with transformers,” in Proc. of the IEEE/CVF Intl. Conf. on Computer Vision (ICCV) , 2023
2023
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M. B. Sariyildiz, K. Alahari, D. Larlus, and Y. Kalantidis, “Fake it till you make it: Learning transferable representations from synthetic imagenet clones,” in Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR) , 2023
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J. Xu, X. Wang, W. Cheng, Y.-P. Cao, Y. Shan, X. Qie, and S. Gao, “Dream3D: Zero-Shot Text-to-3D Synthesis Using 3D Shape Prior and Text-to-Image Diffusion Models,” in Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR) , 2023
2023
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L. Zhang, A. Rao, and M. Agrawala, “Adding conditional control to text-to-image diffusion models,” in Proc. of the IEEE/CVF Intl. Conf. on Computer Vision (ICCV) , 2023
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B. Cheng, I. Misra, A. G. Schwing, A. Kirillov, and R. Girdhar, “Masked-attention mask transformer for universal image segmentation,” in Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR) , 2022
2022
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T. Karras, M. Aittala, T. Aila, and S. Laine, “Elucidating the design space of diffusion-based generative models,” in Proc. of the Conf. on Neural Information Processing Systems (NeurIPS) , 2022
2022
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D. Lee, C. Kim, S. Kim, M. Cho, and W. Han, “Autoregressive Image Generation Using Residual Quantization,” in Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR) , 2022
2022
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Y. Liao, J. Xie, and A. Geiger, “KITTI-360: A novel dataset and benchmarks for urban scene understanding in 2d and 3d,” IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI) , vol. 45, no. 3, pp. 3292–3310, 2022
2022
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C. Lu, Y. Zhou, F. Bao, J. Chen, C. Li, and J. Zhu, “DPM-solver: A fast ODE solver for diffusion probabilistic model sampling in around 10 steps,” in Proc. of the Conf. on Neural Information Processing Systems (NeurIPS) , 2022
2022
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Z. Lyu, Z. Kong, X. XU, L. Pan, and D. Lin, “A conditional point diffusion-refinement paradigm for 3d point cloud completion,” in Proc. of the Intl. Conf. on Learning Representations (ICLR) , 2022
2022
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L. Nunes, R. Marcuzzi, X. Chen, J. Behley, and C. Stachniss, “SegContrast: 3D Point Cloud Feature Representation Learning through Self-supervised Segment Discrimination,” IEEE Robotics and Automation Letters (RA-L) , vol. 7, no. 2, pp. 2116–2123, 2022
2022
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R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-Resolution Image Synthesis With Latent Diffusion Models,” in Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR) , 2022
2022
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Y. Zhou, B. Liu, Y. Zhu, X. Yang, C. Chen, and J. Xu, “Shifted Diffusion for Text-to-Image Generation,” in Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR) , 2023
2023
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R. Marcuzzi, L. Nunes, L. Wiesmann, J. Behley, and C. Stachniss, “Mask-Based Panoptic LiDAR Segmentation for Autonomous Driving,”
2023
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R. Marcuzzi, L. Nunes, L. Wiesmann, E. Marks, J. Behley, and C. Stachniss, “Mask4D: End-to-End Mask-Based 4D Panoptic Segmentation for LiDAR Sequences,”
2023
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2023
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2024
Later among the works it cites.
J. Lee, S. Lee, C. Jo, W. Im, J. Seon, and S.-E. Yoon, “Semcity: Semantic scene generation with triplane diffusion,” in Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR) , 2024
2024
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Y. Liu, X. Li, X. Li, L. Qi, C. Li, and M.-H. Yang, “Pyramid diffusion for fine 3d large scene generation,” in Proc. of the Europ. Conf. on Computer Vision (ECCV) , 2024
2024
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S. Mo, F. Mu, K. H. Lin, Y. Liu, B. Guan, Y. Li, and B. Zhou, “FreeControl: Training-Free Spatial Control of Any Text-to-Image Diffusion Model with Any Condition,” in Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR) , 2024
2024
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L. Nunes, R. Marcuzzi, B. Mersch, J. Behley, and C. Stachniss, “Scaling Diffusion Models to Real-World 3D LiDAR Scene Completion,” in Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR) , 2024
2024
Later among the works it cites.
Y. Pan, X. Zhong, L. Wiesmann, T. Posewsky, J. Behley, and C. Stachniss, “PIN-SLAM: LiDAR SLAM Using a Point-Based Implicit Neural Representation for Achieving Global Map Consistency,” IEEE Trans. on Robotics (TRO) , vol. 40, pp. 4045–4064, 2024
2024
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H. Ran, V. Guizilini, and Y. Wang, “Towards realistic scene generation with lidar diffusion models,” in Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR) , 2024
2024
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X. Ren, J. Huang, X. Zeng, K. Museth, S. Fidler, and F. Williams, “Xcube: Large-scale 3d generative modeling using sparse voxel hierarchies,” in Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR) , 2024
2024
Later among the works it cites.
2024
Later among the works it cites.
Y. Tian, L. Fan, K. Chen, D. Katabi, D. Krishnan, and P. Isola, “Learning Vision from Models Rivals Learning Vision from Data,” in Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR) , 2024
2024
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M. Zhang, W. Peng, G. Ding, C. Lei, C. Ji, and Q. Hao, “CTS Sim-To-Real Unsupervised Domain Adaptation on 3D Detection,” in Proc. of the IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS) , 2024
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
J. Lee, S. Lee, C. Jo, W. Im, J. Seon, and S.-E. Yoon, “Semcity: Semantic scene generation with triplane diffusion,” in
2024
Later among the works it cites.
Y. Liu, X. Li, X. Li, L. Qi, C. Li, and M.-H. Yang, “Pyramid diffusion for fine 3d large scene generation,” in
2024
Later among the works it cites.
L. Nunes, R. Marcuzzi, B. Mersch, J. Behley, and C. Stachniss, “Scaling Diffusion Models to Real-World 3D LiDAR Scene Completion,” in
2024
Later among the works it cites.
X. Ren, J. Huang, X. Zeng, K. Museth, S. Fidler, and F. Williams, “Xcube: Large-scale 3d generative modeling using sparse voxel hierarchies,” in
2024
Later among the works it cites.
T. Hua, L. Jiang, Y.-C. Chen, and W. Zhao, “Sat2city: 3d city generation from a single satellite image with cascaded latent diffusion,” in
2025
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Q. Meng, L. Li, M. Nießner, and A. Dai, “Lt3sd: Latent trees for 3d scene diffusion,” in
2025
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