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State-of-the-art multimodal semantic segmentation strategies combining LiDAR and color data are usually designed on top of asymmetric information-sharing schemes and assume that both modalities are always available.
“Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite,”
A. Geiger, P. Lenz, and R. Urtasun, · 2012
Earlier work this paper cites.
“The apolloscape dataset for autonomous driving,”
Xinyu Huang, Xinjing Cheng, Qichuan Geng, Binbin Cao, Dingfu Zhou, Peng Wang, Yuanqing Lin, and Ruigang Yang, · 2018
Earlier work this paper cites.
“Multimodal vehicle detection: fusing 3d-lidar and color camera data,”
Alireza Asvadi, Luis Garrote, Cristiano Premebida, Paulo Peixoto, and Urbano J. Nunes, · 2018
Earlier work this paper cites.
“SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences,”
J. Behley, M. Garbade, A. Milioto, J. Quenzel, S. Behnke, C. Stachniss, and J. Gall, · 2019
Earlier work this paper cites.
“Robust semantic segmentation in adverse weather conditions by means of sensor data fusion,”
Andreas Pfeuffer and Klaus C. J. Dietmayer, · 2019
Earlier work this paper cites.
“A unified point-based framework for 3d segmentation,”
Hungyueh Chiang, Yenliang Lin, Yuehcheng Liu, and Winston Hsu, · 2019
Earlier work this paper cites.
“Multi-view pointnet for 3d scene understanding,”
Maximilian Jaritz, Jiayuan Gu, and Hao Su, · 2019
Earlier work this paper cites.
“Incremental learning techniques for semantic segmentation,”
Umberto Michieli and Pietro Zanuttigh, · 2019
Earlier work this paper cites.
“Semantickitti: A dataset for semantic scene understanding of lidar sequences,”
Jens Behley, Martin Garbade, Andres Milioto, Jan Quenzel, Sven Behnke, Cyrill Stachniss, and Jurgen Gall, · 2019
Earlier work this paper cites.
“nuscenes: A multimodal dataset for autonomous driving,”
Holger Caesar, Varun Bankiti, Alex H Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom, · 2020
Cited alongside, same era.
“Multimodal deep-learning for object recognition combining camera and lidar data,”
Gledson Melotti, Cristiano Premebida, and Nuno Gonçalves, · 2020
Cited alongside, same era.
“Fuseseg: Lidar point cloud segmentation fusing multi-modal data,”
Georg Krispel, Michael Opitz, Georg Waltner, Horst Possegger, and Horst Bischof, · 2020
Cited alongside, same era.
“xmuda: Cross-modal unsupervised domain adaptation for 3d semantic segmentation,”
Maximilian Jaritz, Tuan-Hung Vu, Raoul de Charette, Emilie Wirbel, and Patrick Pérez, · 2020
Cited alongside, same era.
“Modeling the background for incremental learning in semantic segmentation,”
Fabio Cermelli, Massimiliano Mancini, Samuel Rota Bulo, Elisa Ricci, and Barbara Caputo, · 2020
Cited alongside, same era.
“Perception-aware multi-sensor fusion for 3d lidar semantic segmentation,”
Zhuangwei Zhuang, Rong Li, Kui Jia, Qicheng Wang, Yuanqing Li, and Mingkui Tan, · 2022
Later among the works it cites.
“Multimodal semantic segmentation in autonomous driving: A review of current approaches and future perspectives,”
Giulia Rizzoli, Francesco Barbato, and Pietro Zanuttigh, · 2022
Later among the works it cites.
“Deepfusion: Lidar-camera deep fusion for multi-modal 3d object detection,”
Yingwei Li, Adams Wei Yu, Tianjian Meng, Ben Caine, Jiquan Ngiam, Daiyi Peng, Junyang Shen, Bo Wu, Yifeng Lu, Denny Zhou, Quoc V. Le, Alan Yuille, and Mingxing Tan, · 2022
Later among the works it cites.
“Incremental learning in semantic segmentation from image labels,”
Fabio Cermelli, Dario Fontanel, Antonio Tavera, Marco Ciccone, and Barbara Caputo, · 2022
Later among the works it cites.
“Recent advancements in learning algorithms for point clouds: An updated overview,”
Elena Camuffo, Daniele Mari, and Simone Milani, · 2022
Later among the works it cites.
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“Class-incremental learning for semantic segmentation re-using neither old data nor old labels,”
Marvin Klingner, Andreas Bär, Philipp Donn, and Tim Fingscheidt, · 2020
Cited alongside, same era.
“Salsanext: Fast semantic segmentation of lidar point clouds for autonomous driving,”
Tiago Cortinhal, George Tzelepis, and Eren Erdal Aksoy, · 2020
Cited alongside, same era.
“Plop: Learning without forgetting for continual semantic segmentation,”
Arthur Douillard, Yifu Chen, Arnaud Dapogny, and Matthieu Cord, · 2021
Cited alongside, same era.
“Continual semantic segmentation via repulsion-attraction of sparse and disentangled latent representations,”
Umberto Michieli and Pietro Zanuttigh, · 2021
Cited alongside, same era.
“Selma: Semantic large-scale multimodal acquisitions in variable weather, daytime and viewpoints,”
Paolo Testolina, Francesco Barbato, Umberto Michieli, Marco Giordani, Pietro Zanuttigh, and Michele Zorzi, · 2023
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“Depthformer: Multimodal positional encodings and cross-input attention for transformer-based segmentation networks,”
Francesco Barbato, Giulia Rizzoli, and Pietro Zanuttigh, · 2023
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“Continual learning for lidar semantic segmentation: Class-incremental and coarse-to-fine strategies on sparse data,”
Elena Camuffo and Simone Milani, · 2023
Closest in time.