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Self-driving cars need to understand 3D scenes efficiently and accurately in order to drive safely.
Pagh, R., Rodler, F.F.: Cuckoo Hashing. Journal of Algorithms (2001)
2001
Earlier work this paper cites.
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: A Large-Scale Hierarchical Image Database. In: CVPR (2009)
2009
Earlier work this paper cites.
Geiger, A., Lenz, P., Urtasun, R.: Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite. In: CVPR (2012)
2012
Earlier work this paper cites.
Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets Robotics: The KITTI Dataset. IJRR (2013)
2013
Earlier work this paper cites.
Chang, A.X., Funkhouser, T., Guibas, L., Hanrahan, P., Huang, Q., Li, Z., Savarese, S., Savva, M., Song, S., Su, H., Xiao, J., Yi, L., Yu, F.: ShapeNet: An Information-Rich 3D Model Repository. arXiv (2015)
2015
Earlier work this paper cites.
Maturana, D., Scherer, S.: VoxNet: A 3D Convolutional Neural Network for Real-Time Object Recognition. In: IROS (2015)
2015
Earlier work this paper cites.
Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., Keutzer, K.: SqueezeNet: AlexNet-Level Accuracy with 50x Fewer Parameters and < < 0.5MB Model Size. arXiv (2016)
2016
Earlier work this paper cites.
Qi, C.R., Su, H., Niessner, M., Dai, A., Yan, M., Guibas, L.J.: Volumetric and Multi-View CNNs for Object Classification on 3D Data. In: CVPR (2016)
2016
Earlier work this paper cites.
Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv (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: CVPR (2017)
2017
Earlier work this paper cites.
Qi, C.R., Yi, L., Su, H., Guibas, L.J.: PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space. In: NeurIPS (2017)
2017
Earlier work this paper cites.
Riegler, G., Ulusoy, A.O., Geiger, A.: OctNet: Learning Deep 3D Representations at High Resolutions. In: CVPR (2017)
2017
Earlier work this paper cites.
Wang, P.S., Liu, Y., Guo, Y.X., Sun, C.Y., Tong, X.: O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis. In: SIGGRAPH (2017)
2017
Earlier work this paper cites.
Zoph, B., Le, Q.V.: Neural Architecture Search with Reinforcement Learning. In: ICLR (2017)
2017
Earlier work this paper cites.
Graham, B., Engelcke, M., van der Maaten, L.: 3D Semantic Segmentation With Submanifold Sparse Convolutional Networks. In: CVPR (2018)
2018
Earlier work this paper cites.
He, Y., Lin, J., Liu, Z., Wang, H., Li, L.J., Han, S.: AMC: AutoML for Model Compression and Acceleration on Mobile Devices. In: ECCV (2018)
2018
Earlier work this paper cites.
Landrieu, L., Simonovsky, M.: Large-Scale Point Cloud Semantic Segmentation With Superpoint Graphs. In: CVPR (2018)
2018
Earlier work this paper cites.
Li, Y., Bu, R., Sun, M., Wu, W., Di, X., Chen, B.: PointCNN: Convolution on 𝒳 \mathcal{X} -Transformed Points. In: NeurIPS (2018)
2018
Earlier work this paper cites.
Liu, C., Zoph, B., Neumann, M., Shlens, J., Hua, W., Li, L.J., Fei-Fei, L., Yuille, A., Huang, J., Murphy, K.: Progressive Neural Architecture Search. In: ECCV (2018)
2018
Earlier work this paper cites.
Ma, N., Zhang, X., Zheng, H.T., Sun, J.: ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design. In: ECCV (2018)
2018
Earlier work this paper cites.
Qi, C.R., Liu, W., Wu, C., Su, H., Guibas, L.J.: Frustum PointNets for 3D Object Detection from RGB-D Data. In: CVPR (2018)
2018
Earlier work this paper cites.
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: MobileNetV2: Inverted Residuals and Linear Bottlenecks. In: CVPR (2018)
2018
Earlier work this paper cites.
Su, H., Jampani, V., Sun, D., Maji, S., Kalogerakis, E., Yang, M.H., Kautz, J.: SPLATNet: Sparse Lattice Networks for Point Cloud Processing. In: CVPR (2018)
2018
Earlier work this paper cites.
Tatarchenko, M., Park, J., Koltun, V., Zhou, Q.Y.: Tangent Convolutions for Dense Prediction in 3D. In: CVPR (2018)
2018
Earlier work this paper cites.
Wang, P.S., Liu, Y., Guo, Y.X., Sun, C.Y., Tong, X.: Adaptive O-CNN: A Patch-based Deep Representation of 3D Shapes. In: SIGGRAPH Asia (2018)
2018
Earlier work this paper cites.
Xu, Y., Fan, T., Xu, M., Zeng, L., Qiao, Y.: SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters. In: ECCV (2018)
2018
Earlier work this paper cites.
Yan, Y., Mao, Y., Li, B.: SECOND: Sparsely Embedded Convolutional Detection. Sensors (2018)
2018
Earlier work this paper cites.
Zhang, X., Zhou, X., Lin, M., Sun, J.: ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. In: CVPR (2018)
2018
Cited alongside, same era.
Zhou, Y., Tuzel, O.: VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection. In: CVPR (2018)
2018
Cited alongside, same era.
Zoph, B., Vasudevan, V., Shlens, J., Le, Q.V.: Learning Transferable Architectures for Scalable Image Recognition. In: CVPR (2018)
2018
Cited alongside, same era.
Bae, W., Lee, S., Lee, Y., Park, B., Chung, M., Jung, K.H.: Resource Optimized Neural Architecture Search for 3D Medical Image Segmentation. In: MICCAI (2019)
2019
Cited alongside, same era.
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: ICCV (2019)
2019
Wong, K.C., Moradi, M.: SegNAS3D: Network Architecture Search with Derivative-Free Global Optimization for 3D Image Segmentation. In: MICCAI (2019)
2019
Later among the works it cites.
Wu, B., Dai, X., Zhang, P., Wang, Y., Sun, F., Wu, Y., Tian, Y., Vajda, P., Jia, Y., Keutzer, K.: FBNet: Hardware-aware Efficient Convnet Design via Differentiable Neural Architecture Search. In: CVPR (2019)
2019
Later among the works it cites.
Wu, W., Qi, Z., Fuxin, L.: PointConv: Deep Convolutional Networks on 3D Point Clouds. In: CVPR (2019)
2019
Later among the works it cites.
Yang, B., Wang, J., Clark, R., Hu, Q., Wang, S., Markham, A., Trigoni, N.: Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds. In: NeurIPS (2019)
2019
Later among the works it cites.
Yang, D., Roth, H., Xu, Z., Milletari, F., Zhang, L., Xu, D.: Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation. In: MICCAI (2019)
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Cited alongside, same era.
Cai, H., Lin, J., Lin, Y., Liu, Z., Wang, K., Wang, T., Zhu, L., Han, S.: AutoML for Architecting Efficient and Specialized Neural Networks. IEEE Micro (2019)
2019
Cited alongside, same era.
Cai, H., Zhu, L., Han, S.: ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware. In: ICLR (2019)
2019
Cited alongside, same era.
Chen, Y., Yang, T., Zhang, X., Meng, G., Xiao, X., Sun, J.: DetNAS: Backbone Search for Object Detection. In: NeurIPS (2019)
2019
Cited alongside, same era.
Choy, C., Gwak, J., Savarese, S.: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. In: CVPR (2019)
2019
Cited alongside, same era.
Kim, S., Kim, I., Lim, S., Baek, W., Kim, C., Cho, H., Yoon, B., Kim, T.: Scalable Neural Architecture Search for 3D Medical Image Segmentation. In: MICCAI (2019)
2019
Cited alongside, same era.
Lahoud, J., Ghanem, B., Pollefeys, M., Oswald, M.R.: 3D Instance Segmentation via Multi-Task Metric Learning. In: ICCV (2019)
2019
Cited alongside, same era.
Lei, H., Akhtar, N., Mian, A.: Octree Guided CNN With Spherical Kernels for 3D Point Clouds. In: CVPR (2019)
2019
Cited alongside, same era.
2019
Later among the works it cites.
Yang, Z., Sun, Y., Liu, S., Shen, X., Jia, J.: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. In: ICCV (2019)
2019
Later among the works it cites.
Zhu, Z., Liu, C., Yang, D., Yuille, A., Xu, D.: V-NAS: Neural Architecture Search for Volumetric Medical Image Segmentation. In: 3DV (2019)
2019
Later among the works it cites.
Alonso, I., Riazuelo, L., Montesano, L., Murillo, A.C.: 3D-MiniNet: Learning a 2D Representation from Point Clouds for Fast and Efficient 3D LIDAR Semantic Segmentation. arXiv (2020)
2020
Closest in time.
Cai, H., Gan, C., Wang, T., Zhang, Z., Han, S.: Once for All: Train One Network and Specialize it for Efficient Deployment. In: ICLR (2020)
2020
Closest in time.
Cortinhal, T., Tzelepis, G., Aksoy, E.E.: SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving. arXiv (2020)
2020
Closest in time.
Guo, Z., Zhang, X., Mu, H., Heng, W., Liu, Z., Wei, Y., Sun, J.: Single Path One-Shot Neural Architecture Search with Uniform Sampling. In: ECCV (2020)
2020
Closest in time.
Han, L., Zheng, T., Xu, L., Fang, L.: OccuSeg: Occupancy-aware 3D Instance Segmentation. In: CVPR (2020)
2020
Closest in time.
Hu, Q., Yang, B., Xie, L., Rosa, S., Guo, Y., Wang, Z., Trigoni, N., Markham, A.: RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds. In: CVPR (2020)
2020
Closest in time.
Jiang, L., Zhao, H., Shi, S., Liu, S., Fu, C.W., Jia, J.: PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation. In: CVPR (2020)
2020
Closest in time.
Li, G., Qian, G., Delgadillo, I.C., Muller, M., Thabet, A., Ghanem, B.: SGAS: Sequential Greedy Architecture Search. In: CVPR (2020)
2020
Closest in time.
Li, M., Lin, J., Ding, Y., Liu, Z., Zhu, J.Y., Han, S.: GAN Compression: Efficient Architectures for Interactive Conditional GANs. In: CVPR (2020)
2020
Closest in time.
Ma, Z., Zhou, Z., Liu, Y., Lei, Y., Yan, H.: Auto-ORVNet: Orientation-Boosted Volumetric Neural Architecture Search for 3D Shape Classification. IEEE Access (2020)
2020
Closest in time.
Qi, C.R., Chen, X., Litany, O., Guibas, L.J.: ImVoteNet: Boosting 3D Object Detection in Point Clouds with Image Votes. In: CVPR (2020)
2020
Closest in time.
Shi, S., Guo, C., Jiang, L., Wang, Z., Shi, J., Wang, X., Li, H.: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. In: CVPR (2020)
2020
Closest in time.
Shi, S., Wang, Z., Shi, J., Wang, X., Li, H.: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. TPAMI (2020)
2020
Closest in time.
Wang, H., Wu, Z., Liu, Z., Cai, H., Zhu, L., Gan, C., Han, S.: HAT: Hardware-Aware Transformers for Efficient Natural Language Processing. In: ACL (2020)
2020
Closest in time.
Wang, K., Liu, Z., Lin, Y., Lin, J., Han, S.: Hardware-Centric AutoML for Mixed-Precision Quantization. IJCV (2020)
2020
Closest in time.
Wang, T., Wang, K., Cai, H., Lin, J., Liu, Z., Wang, H., Lin, Y., Han, S.: APQ: Joint Search for Network Architecture, Pruning and Quantization Policy. In: CVPR (2020)
2020
Closest in time.
Xu, C., Wu, B., Wang, Z., Zhan, W., Vajda, P., Keutzer, K., Tomizuka, M.: SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation. In: ECCV (2020)
2020
Closest in time.
Yu, Q., Yang, D., Roth, H., Bai, Y., Zhang, Y., Yuille, A., Xu, D.: C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation. In: CVPR (2020)
2020
Closest in time.
Zhang, Y., Zhou, Z., David, P., Yue, X., Xi, Z., Gong, B., Foroosh, H.: PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation. In: CVPR (2020)
2020
Closest in time.