Fetching the paper…
Reading the bibliography…
Neural Scene Flow Prior (NSFP) and Fast Neural Scene Flow (FNSF) have shown remarkable adaptability in the context of large out-of-distribution autonomous driving.
Sequential operations in digital picture processing
A. Rosenfeld and J. L. Pfaltz · 1966
Earlier work this paper cites.
Distribution-free inequalities for the deleted and holdout error estimates
L. Devroye and T. Wagner · 1979
Earlier work this paper cites.
Euclidean distance mapping
P.-E. Danielsson · 1980
Earlier work this paper cites.
Linear time euclidean distance transform algorithms
H. Breu, J. Gil, D. Kirkpatrick, and M. Werman · 1995
Earlier work this paper cites.
Multi-frame optical flow estimation using subspace constraints
M. Irani · 1999
Earlier work this paper cites.
Rademacher and gaussian complexities: Risk bounds and structural results
P. L. Bartlett and S. Mendelson · 2002
Earlier work this paper cites.
Stability and generalization
O. Bousquet and A. Elisseeff · 2002
Earlier work this paper cites.
Covering number bounds of certain regularized linear function classes
T. Zhang · 2002
Earlier work this paper cites.
Voxnet: A 3d convolutional neural network for real-time object recognition
D. Maturana and S. Scherer · 2015
Earlier work this paper cites.
Object scene flow for autonomous vehicles
M. Menze and A. Geiger · 2015
Earlier work this paper cites.
3D ShapeNets: A deep representation for volumetric shapes
Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, and J. Xiao · 2015
Earlier work this paper cites.
On large-batch training for deep learning: Generalization gap and sharp minima
N. S. Keskar, D. Mudigere, J. Nocedal, M. Smelyanskiy, and P. T. P. Tang · 2016
Earlier work this paper cites.
Algorithm-dependent generalization bounds for multi-task learning
T. Liu, D. Tao, M. Song, and S. J. Maybank · 2016
Earlier work this paper cites.
A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation
N. Mayer, E. Ilg, P. Hausser, P. Fischer, D. Cremers, A. Dosovitskiy, and T. Brox · 2016
Earlier work this paper cites.
Direct sparse odometry
J. Engel, V. Koltun, and D. Cremers · 2017
Earlier work this paper cites.
A point set generation network for 3d object reconstruction from a single image
H. Fan, H. Su, and L. J. Guibas · 2017
Earlier work this paper cites.
Multiframe scene flow with piecewise rigid motion
V. Golyanik, K. Kim, R. Maier, M. Nießner, D. Stricker, and J. Kautz · 2017
Earlier work this paper cites.
Escape from cells: Deep kd-networks for the recognition of 3d point cloud models
R. Klokov and V. Lempitsky · 2017
Earlier work this paper cites.
PointNet: Deep learning on point sets for 3D classification and segmentation
C. R. Qi, H. Su, K. Mo, and L. J. Guibas · 2017
Earlier work this paper cites.
Pointnet++: Deep hierarchical feature learning on point sets in a metric space
C. R. Qi, L. Yi, H. Su, and L. J. Guibas · 2017
Earlier work this paper cites.
Octnet: Learning deep 3d representations at high resolutions
G. Riegler, A. Osman Ulusoy, and A. Geiger · 2017
Earlier work this paper cites.
Optical flow in mostly rigid scenes
J. Wulff, L. Sevilla-Lara, and M. J. Black · 2017
Earlier work this paper cites.
Unsupervised learning of multi-frame optical flow with occlusions
J. Janai, F. Guney, A. Ranjan, M. Black, and A. Geiger · 2018
Earlier work this paper cites.
Visualizing the loss landscape of neural nets
H. Li, Z. Xu, G. Taylor, C. Studer, and T. Goldstein · 2018
Earlier work this paper cites.
PointCNN: Convolution on X-transformed points
Y. Li, R. Bu, M. Sun, W. Wu, X. Di, and B. Chen · 2018
Earlier work this paper cites.
Proflow: Learning to predict optical flow
D. Maurer and A. Bruhn · 2018
Earlier work this paper cites.
Foundations of machine learning
M. Mohri, A. Rostamizadeh, and A. Talwalkar · 2018
Earlier work this paper cites.
Voxelnet: End-to-end learning for point cloud based 3d object detection
Y. Zhou and O. Tuzel · 2018
Earlier work this paper cites.
Argoverse: 3D tracking and forecasting with rich maps
M.-F. Chang, J. Lambert, P. Sangkloy, J. Singh, S. Bak, A. Hartnett, D. Wang, P. Carr, S. Lucey, D. Ramanan, et al · 2019
Earlier work this paper cites.
4d spatio-temporal convnets: Minkowski convolutional neural networks
C. Choy, J. Gwak, and S. Savarese · 2019
Earlier work this paper cites.
HPLFlownet: Hierarchical permutohedral lattice flownet for scene flow estimation on large-scale point clouds
X. Gu, Y. Wang, C. Wu, Y. J. Lee, and P. Wang · 2019
Earlier work this paper cites.
SENSE: A shared encoder network for scene-flow estimation
H. Jiang, D. Sun, V. Jampani, Z. Lv, E. Learned-Miller, and J. Kautz · 2019
Cited alongside, same era.
SelFlow: Self-supervised learning of optical flow
P. Liu, M. Lyu, I. King, and J. Xu · 2019
Cited alongside, same era.
FlowNet3D: Learning scene flow in 3D point clouds
X. Liu, C. R. Qi, and L. J. Guibas · 2019
Cited alongside, same era.
MeteorNet: Deep learning on dynamic 3d point cloud sequences
X. Liu, M. Yan, and J. Bohg · 2019
Cited alongside, same era.
Deep rigid instance scene flow
W.-C. Ma, S. Wang, R. Hu, Y. Xiong, and R. Urtasun · 2019
Cited alongside, same era.
A fusion approach for multi-frame optical flow estimation
Z. Ren, O. Gallo, D. Sun, M.-H. Yang, E. Sudderth, and J. Kautz · 2019
Cited alongside, same era.
Point cloud registration using representative overlapping points
L. Zhu, D. Liu, C. Lin, R. Yan, F. Gómez-Fernández, N. Yang, and Z. Feng · 2021
Later among the works it cites.
Point clouds downsampling based on complementary attention and contrastive learning
C. Chen, D. Liu, and C. Xu · 2022
Later among the works it cites.
Transfuser: Imitation with transformer-based sensor fusion for autonomous driving
K. Chitta, A. Prakash, B. Jaeger, Z. Yu, K. Renz, and A. Geiger · 2022
Later among the works it cites.
Exploiting rigidity constraints for lidar scene flow estimation
G. Dong, Y. Zhang, H. Li, X. Sun, and Z. Xiong · 2022
Later among the works it cites.
Deformation and correspondence aware unsupervised synthetic-to-real scene flow estimation for point clouds
Z. Jin, Y. Lei, N. Akhtar, H. Li, and M. Hayat · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
V. Sarode, X. Li, H. Goforth, Y. Aoki, R. A. Srivatsan, S. Lucey, and H. Choset · 2019
Cited alongside, same era.
Kpconv: Flexible and deformable convolution for point clouds
H. Thomas, C. R. Qi, J.-E. Deschaud, B. Marcotegui, F. Goulette, and L. J. Guibas · 2019
Cited alongside, same era.
Dynamic graph CNN for learning on point clouds
Y. Wang, Y. Sun, Z. Liu, S. E. Sarma, M. M. Bronstein, and J. M. Solomon · 2019
Cited alongside, same era.
PointConv: Deep convolutional networks on 3d point clouds
W. Wu, Z. Qi, and L. Fuxin · 2019
Cited alongside, same era.
3d point cloud denoising using graph laplacian regularization of a low dimensional manifold model
J. Zeng, G. Cheung, M. Ng, J. Pang, and C. Yang · 2019
Cited alongside, same era.
Samplenet: Differentiable point cloud sampling
I. Lang, A. Manor, and S. Avidan · 2020
Cited alongside, same era.
Pointfp: A feature-preserving point cloud sampling
D. Liu, C. Chen, S. Liu, Z. Jiang, and C. Xu · 2022
Later among the works it cites.
A robust and reliable point cloud recognition network under rigid transformation
D. Liu, C. Chen, C. Xu, Q. Cai, L. Chu, F. Wen, and R. Qiu · 2022
Later among the works it cites.
Pfmixer: Point cloud frequency mixing
D. Liu, S. Liu, C. Chen, Z. Jiang, and C. Xu · 2022
Later among the works it cites.
Motion inspired unsupervised perception and prediction in autonomous driving
M. Najibi, J. Ji, Y. Zhou, C. R. Qi, X. Yan, S. Ettinger, and D. Anguelov · 2022
Later among the works it cites.
Pointglr: Unsupervised structural representation learning of 3d point clouds
Y. Rao, J. Lu, and J. Zhou · 2022
Later among the works it cites.
Road: The road event awareness dataset for autonomous driving
G. Singh, S. Akrigg, M. Di Maio, V. Fontana, R. J. Alitappeh, S. Khan, S. Saha, K. Jeddisaravi, F. Yousefi, J. Culley, et al · 2022
Later among the works it cites.
Neural prior for trajectory estimation
C. Wang, X. Li, J. K. Pontes, and S. Lucey · 2022
Later among the works it cites.
What matters for 3d scene flow network
G. Wang, Y. Hu, Z. Liu, Y. Zhou, M. Tomizuka, W. Zhan, and H. Wang · 2022
Later among the works it cites.
Efficient 3d deep lidar odometry
G. Wang, X. Wu, S. Jiang, Z. Liu, and H. Wang · 2022
Later among the works it cites.
Saks: Sampling adaptive kernels from subspace for point cloud graph convolution
C. Chen, D. Liu, C. Xu, and T.-K. Truong · 2023
Later among the works it cites.
Re-evaluating lidar scene flow for autonomous driving
N. Chodosh, D. Ramanan, and S. Lucey · 2023
Later among the works it cites.
Fear-neuro-inspired reinforcement learning for safe autonomous driving
X. He, J. Wu, Z. Huang, Z. Hu, J. Wang, A. Sangiovanni-Vincentelli, and C. Lv · 2023
Later among the works it cites.
Fuller: Unified multi-modality multi-task 3d perception via multi-level gradient calibration
Z. Huang, S. Lin, G. Liu, M. Luo, C. Ye, H. Xu, X. Chang, and X. Liang · 2023
Later among the works it cites.
Scoop: Self-supervised correspondence and optimization-based scene flow
I. Lang, D. Aiger, F. Cole, S. Avidan, and M. Rubinstein · 2023
Later among the works it cites.
Textslam: Visual slam with semantic planar text features
B. Li, D. Zou, Y. Huang, X. Niu, L. Pei, and W. Yu · 2023
Later among the works it cites.
Fast neural scene flow
X. Li, J. Zheng, F. Ferroni, J. K. Pontes, and S. Lucey · 2023
Later among the works it cites.
Self-supervised point cloud registration with deep versatile descriptors for intelligent driving
D. Liu, C. Chen, C. Xu, R. C. Qiu, and L. Chu · 2023
Later among the works it cites.
Towards the difficulty for a deep neural network to learn concepts of different complexities
D. Liu, H. Deng, X. Cheng, Q. Ren, K. Wang, and Q. Zhang · 2023
Later among the works it cites.
Controllable mesh generation through sparse latent point diffusion models
Z. Lyu, J. Wang, Y. An, Y. Zhang, D. Lin, and B. Dai · 2023
Later among the works it cites.
M-fuse: Multi-frame fusion for scene flow estimation
L. Mehl, A. Jahedi, J. Schmalfuss, and A. Bruhn · 2023
Later among the works it cites.
Delflow: Dense efficient learning of scene flow for large-scale point clouds
C. Peng, G. Wang, X. W. Lo, X. Wu, C. Xu, M. Tomizuka, W. Zhan, and H. Wang · 2023
Later among the works it cites.
Zeroflow: Fast zero label scene flow via distillation
K. Vedder, N. Peri, N. Chodosh, I. Khatri, E. Eaton, D. Jayaraman, Y. Liu, D. Ramanan, and J. Hays · 2023
Later among the works it cites.
3d point-voxel correlation fields for scene flow estimation
Z. Wang, Y. Wei, Y. Rao, J. Zhou, and J. Lu · 2023
Later among the works it cites.
Gmsf: Global matching scene flow
Y. Zhang, J. Edstedt, B. Wandt, P.-E. Forssén, M. Magnusson, and M. Felsberg · 2023
Later among the works it cites.
Neuralpci: Spatio-temporal neural field for 3d point cloud multi-frame non-linear interpolation
Z. Zheng, D. Wu, R. Lu, F. Lu, G. Chen, and C. Jiang · 2023
Later among the works it cites.