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Estimating the complete 3D point cloud from an incomplete one is a key problem in many vision and robotics applications.
Vision meets robotics: The KITTI dataset
Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: · 2013
Earlier work this paper cites.
3D ShapeNets: A deep representation for volumetric shapes
Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., Xiao, J.: · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D.P., Ba, J.: · 2015
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Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age
Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., Reid, I.D., Leonard, J.J.: · 2016
Earlier work this paper cites.
VConv-DAE: Deep volumetric shape learning without object labels
Sharma, A., Grau, O., Fritz, M.: · 2016
Earlier work this paper cites.
A field model for repairing 3D shapes
Nguyen, D.T., Hua, B., Tran, M., Pham, Q., Yeung, S.: · 2016
Earlier work this paper cites.
Shape completion using 3D-encoder-predictor CNNs and shape synthesis
Dai, A., Qi, C.R., Nießner, M.: · 2017
Earlier work this paper cites.
High-resolution shape completion using deep neural networks for global structure and local geometry inference
Han, X., Li, Z., Huang, H., Kalogerakis, E., Yu, Y.: · 2017
Earlier work this paper cites.
Shape completion enabled robotic grasping
Varley, J., DeChant, C., Richardson, A., Ruales, J., Allen, P.K.: · 2017
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Kipf, T.N., Welling, M.: · 2017
Earlier work this paper cites.
A point set generation network for 3D object reconstruction from a single image
Fan, H., Su, H., Guibas, L.J.: · 2017
Earlier work this paper cites.
Learning a multi-view stereo machine
Kar, A., Häne, C., Malik, J.: · 2017
Earlier work this paper cites.
PointNet: Deep learning on point sets for 3D classification and segmentation
Qi, C.R., Su, H., Mo, K., Guibas, L.J.: · 2017
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PointNet++: Deep hierarchical feature learning on point sets in a metric space
Qi, C.R., Yi, L., Su, H., Guibas, L.J.: · 2017
Earlier work this paper cites.
Shape completion from a single RGBD image
Li, D., Shao, T., Wu, H., Zhou, K.: · 2017
Earlier work this paper cites.
Learning 3D shape completion from laser scan data with weak supervision
Stutz, D., Geiger, A.: · 2018
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PCN: point completion network
Yuan, W., Khot, T., Held, D., Mertz, C., Hebert, M.: · 2018
Earlier work this paper cites.
Splatnet: Sparse lattice networks for point cloud processing
Su, H., Jampani, V., Sun, D., Maji, S., Kalogerakis, E., Yang, M., Kautz, J.: · 2018
Earlier work this paper cites.
GAL: geometric adversarial loss for single-view 3D-object reconstruction
Jiang, L., Shi, S., Qi, X., Jia, J.: · 2018
Cited alongside, same era.
Efficient dense point cloud object reconstruction using deformation vector fields
Li, K., Pham, T., Zhan, H., Reid, I.D.: · 2018
Cited alongside, same era.
Learning efficient point cloud generation for dense 3D object reconstruction
Lin, C., Kong, C., Lucey, S.: · 2018
Cited alongside, same era.
Learning representations and generative models for 3D point clouds
Achlioptas, P., Diamanti, O., Mitliagkas, I., Guibas, L.J.: · 2018
Cited alongside, same era.
A papier-mâché approach to learning 3D surface generation
Groueix, T., Fisher, M., Kim, V.G., Russell, B.C., Aubry, M.: · 2018
Cited alongside, same era.
Pointwise convolutional neural networks
Hua, B., Tran, M., Yeung, S.: · 2018
Pvnet: Pixel-wise voting network for 6dof pose estimation
Peng, S., Liu, Y., Huang, Q., Zhou, X., Bao, H.: · 2019
Later among the works it cites.
Justlookup: One millisecond deep feature extraction for point clouds by lookup tables
Lin, H., Xiao, Z., Tan, Y., Chao, H., Ding, S.: · 2019
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Linked Dynamic Graph CNN: learning on point cloud via linking hierarchical features
Zhang, K., Hao, M., Wang, J., de Silva, C.W., Fu, C.: · 2019
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Unsupervised multi-task feature learning on point clouds
Hassani, K., Haley, M.: · 2019
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VoxSegNet: Volumetric CNNs for semantic part segmentation of 3D shapes
Wang, Z., Lu, F.: · 2019
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Octree guided CNN with spherical kernels for 3D point clouds
Lei, H., Akhtar, N., Mian, A.: · 2019
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Cited alongside, same era.
PointCNN: Convolution on x-transformed points
Li, Y., Bu, R., Sun, M., Wu, W., Di, X., Chen, B.: · 2018
Cited alongside, same era.
SpiderCNN: Deep learning on point sets with parameterized convolutional filters
Xu, Y., Fan, T., Xu, M., Zeng, L., Qiao, Y.: · 2018
Cited alongside, same era.
Monte carlo convolution for learning on non-uniformly sampled point clouds
Hermosilla, P., Ritschel, T., Vázquez, P., Vinacua, A., Ropinski, T.: · 2018
Cited alongside, same era.
FoldingNet: Point cloud auto-encoder via deep grid deformation
Yang, Y., Feng, C., Shen, Y., Tian, D.: · 2018
Cited alongside, same era.
Point-voxel CNN for efficient 3D deep learning
Liu, Z., Tang, H., Lin, Y., Han, S.: · 2019
Cited alongside, same era.
Dense 3D point cloud reconstruction using a deep pyramid nxetwork
Mandikal, P., Radhakrishnan, V.B.: · 2019
Cited alongside, same era.
Later among the works it cites.
Modeling local geometric structure of 3D point clouds using Geo-CNN
Lan, S., Yu, R., Yu, G., Davis, L.S.: · 2019
Later among the works it cites.
Relation-shape convolutional neural network for point cloud analysis
Liu, Y., Fan, B., Xiang, S., Pan, C.: · 2019
Later among the works it cites.
DensePoint: Learning densely contextual representation for efficient point cloud processing
Liu, Y., Fan, B., Meng, G., Lu, J., Xiang, S., Pan, C.: · 2019
Later among the works it cites.
PointConv: Deep convolutional networks on 3D point clouds
Wu, W., Qi, Z., Li, F.: · 2019
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Pix2Vox: Context-aware 3D reconstruction from single and multi-view images
Xie, H., Yao, H., Sun, X., Zhou, S., Zhang, S.: · 2019
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What do single-view 3D reconstruction networks learn?
Tatarchenko, M., Richter, S.R., Ranftl, R., Li, Z., Koltun, V., Brox, T.: · 2019
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PyTorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.a.: · 2019
Later among the works it cites.
PU-GAN: a point cloud upsampling adversarial network
Li, R., Li, X., Fu, C., Cohen-Or, D., Heng, P.: · 2019
Later among the works it cites.
Morphing and sampling network for dense point cloud completion
Liu, M., Sheng, L., Yang, S., Shao, J., Hu, S.M.: · 2020
Closest in time.
Pix2Vox++: Multi-scale context-aware 3D object reconstruction from single and multiple images
Xie, H., Yao, H., Zhang, S., Zhou, S., Sun, W.: · 2020
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Learned step size quantization
Esser, S.K., McKinstry, J.L., Bablani, D., Appuswamy, R., Modha, D.S.: · 2020
Closest in time.