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Predicting how the world can evolve in the future is crucial for motion planning in autonomous systems.
A Fast Voxel Traversal Algorithm for Ray Tracing
John Amanatides and Andrew Woo · 1987
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Vision meets robotics: The kitti dataset
Andreas Geiger, Philip Lenz, Christoph Stiller, and Raquel Urtasun · 2013
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Sparsity invariant cnns
Jonas Uhrig, Nick Schneider, Lukas Schneider, Uwe Franke, Thomas Brox, and Andreas Geiger · 2017
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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
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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 · 2019
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Neural volumes: Learning dynamic renderable volumes from images
Stephen Lombardi, Tomas Simon, Jason Saragih, Gabriel Schwartz, Andreas Lehrmann, and Yaser Sheikh · 2019
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End-to-end interpretable neural motion planner
Wenyuan Zeng, Wenjie Luo, Simon Suo, Abbas Sadat, Bin Yang, Sergio Casas, and Raquel Urtasun · 2019
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Domain transfer for semantic segmentation of lidar data using deep neural networks
Ferdinand Langer, Andres Milioto, Alexandre Haag, Jens Behley, and Cyrill Stachniss · 2020
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Nerf: Representing scenes as neural radiance fields for view synthesis
Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng · 2020
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Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision
Michael Niemeyer, Lars Mescheder, Michael Oechsle, and Andreas Geiger · 2020
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Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer
René Ranftl, Katrin Lasinger, David Hafner, Konrad Schindler, and Vladlen Koltun · 2020
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Perceive, predict, and plan: Safe motion planning through interpretable semantic representations
Abbas Sadat, Sergio Casas, Mengye Ren, Xinyu Wu, Pranaab Dhawan, and Raquel Urtasun · 2020
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Inverting the pose forecasting pipeline with spf2: Sequential pointcloud forecasting for sequential pose forecasting
Xinshuo Weng, Jianren Wang, Sergey Levine, Kris Kitani, and Nicholas Rhinehart · 2021
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Argoverse 2.0: Next generation datasets for self-driving perception and forecasting
Benjamin Wilson, William Qi, Tanmay Agarwal, John Lambert, Jagjeet Singh, Siddhesh Khandelwal, Bowen Pan, Ratnesh Kumar, Andrew Hartnett, Jhony Kaesemodel Pontes, et al · 2021
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Complete & label: A domain adaptation approach to semantic segmentation of lidar point clouds
Li Yi, Boqing Gong, and Thomas Funkhouser · 2021
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Differentiable raycasting for self-supervised occupancy forecasting
Tarasha Khurana, Peiyun Hu, Achal Dave, Jason Ziglar, David Held, and Deva Ramanan · 2022
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Occupancy flow fields for motion forecasting in autonomous driving
Reza Mahjourian, Jinkyu Kim, Yuning Chai, Mingxing Tan, Ben Sapp, and Dragomir Anguelov · 2022
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Mp3: A unified model to map, perceive, predict and plan
Sergio Casas, Abbas Sadat, and Raquel Urtasun · 2021
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Vision transformers for dense prediction
René Ranftl, Alexey Bochkovskiy, and Vladlen Koltun · 2021
Cited alongside, same era.
https://www.nuscenes.org/nuscenes#lidarseg
nuScenes LiDARSeg
Cited in the paper.
Self-supervised point cloud prediction using 3d spatio-temporal convolutional networks
Benedikt Mersch, Xieyuanli Chen, Jens Behley, and Cyrill Stachniss · 2022
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S2net: Stochastic sequential pointcloud forecasting
Xinshuo Weng, Junyu Nan, Kuan-Hui Lee, Rowan McAllister, Adrien Gaidon, Nicholas Rhinehart, and Kris M Kitani · 2022
Later among the works it cites.