List of available models
Below is a list and a brief explanation about the models currently available on PTLFlow.
List of models
CCMR
Paper: CCMR: High Resolution Optical Flow Estimation via Coarse-to-Fine Context-Guided Motion Reasoning - https://arxiv.org/abs/2311.02661
Reference code: https://github.com/cv-stuttgart/CCMR
Model names:
ccmr
,ccmr+
CRAFT
Paper: CRAFT: Cross-Attentional Flow Transformers for Robust Optical Flow - https://arxiv.org/abs/2203.16896
Reference code: https://github.com/askerlee/craft
Model names:
craft
CSFlow
Paper: CSFlow: Learning optical flow via cross strip correlation for autonomous driving - https://arxiv.org/abs/2202.00909
Reference code: https://github.com/MasterHow/CSFlow
Model names:
csflow
DICL-Flow
Paper: Displacement-Invariant Matching Cost Learning for Accurate Optical Flow Estimation - https://arxiv.org/abs/2010.14851
Reference code: https://github.com/jytime/DICL-Flow
Model names:
dicl
DIP
Paper: DIP: Deep Inverse Patchmatch for High-Resolution Optical Flow - https://arxiv.org/abs/2204.00330
Reference code: https://github.com/zihuazheng/DIP
Model names:
dip
FastFlownet
Paper: FastFlowNet: A Lightweight Network for Fast Optical Flow Estimation - https://arxiv.org/abs/2103.04524
Reference code: https://github.com/ltkong218/FastFlowNet
Model names:
fastflownet
Flow1D
Paper: High-Resolution Optical Flow from 1D Attention and Correlation - https://arxiv.org/abs/2104.13918
Reference code: https://github.com/haofeixu/flow1d
Model names:
flow1d
FlowFormer
Paper: FlowFormer: A Transformer Architecture for Optical Flow - https://arxiv.org/abs/2203.16194
Reference code: https://github.com/drinkingcoder/FlowFormer-Official
Model names:
flowformer
FlowFormer++
Paper: Flowformer++: Masked cost volume autoencoding for pretraining optical flow estimation - https://arxiv.org/abs/2303.01237
Reference code: https://github.com/XiaoyuShi97/FlowFormerPlusPlus
Model names:
flowformer++
Flownet
Papers:
FlowNet: Learning Optical Flow with Convolutional Networks - https://arxiv.org/abs/1504.06852
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks - https://arxiv.org/abs/1612.01925
Reference code: https://github.com/NVIDIA/flownet2-pytorch
Model names:
flownets
,flownetc
,flownet2
,flownetcs
,flownetcss
,flownetsd
GMA
Paper: Learning to Estimate Hidden Motions with Global Motion Aggregation - https://arxiv.org/abs/2104.02409
Reference code: https://github.com/zacjiang/GMA
Model names:
gma
GMFlow
Paper: GMFlow: Learning Optical Flow via Global Matching - https://arxiv.org/abs/2111.13680
Reference code: https://github.com/haofeixu/gmflow
Model names:
gmflow
,gmflow_refine
GMFlow+, UniMatch
Paper: Unifying Flow, Stereo and Depth Estimation - https://arxiv.org/abs/2211.05783
Reference code: https://github.com/autonomousvision/unimatch
Model names:
gmflow+
,gmflow+_sc2
,gmflow+_sc2_refine6
,unimatch
,unimatch_sc2
,unimatch_sc2_refine6
GMFlowNet
Paper: Global Matching with Overlapping Attention for Optical Flow Estimation - https://arxiv.org/abs/2203.11335
Reference code: https://github.com/xiaofeng94/GMFlowNet
Model names:
gmflownet
,gmflownet_mix
HD3
Paper: Hierarchical Discrete Distribution Decomposition for Match Density Estimation - https://arxiv.org/abs/1812.06264
Reference code: https://github.com/ucbdrive/hd3
Model names:
hd3
,hd3_ctxt
IRR
Paper: Iterative Residual Refinement for Joint Optical Flow and Occlusion Estimation - https://arxiv.org/abs/1904.05290
Reference code: https://github.com/visinf/irr
Model names:
irr_pwc
,irr_pwcnet
,irr_pwcnet_irr
LCV
Paper: Learnable Cost Volume Using the Cayley Representation - https://arxiv.org/abs/2007.11431
Reference code: https://github.com/Prinsphield/LCV
Model names:
lcv_raft
,lcv_raft_small
LiteFlowNet
Paper: LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation - https://arxiv.org/abs/1805.07036
Reference code: https://github.com/twhui/LiteFlowNet
Model name:
liteflownet
LiteFlowNet2
Paper: A Lightweight Optical Flow CNN - Revisiting Data Fidelity and Regularization - https://ieeexplore.ieee.org/document/9018073
Reference code: https://github.com/twhui/LiteFlowNet2
Model names:
liteflownet2
,liteflownet2_pseudoreg
LiteFlowNet3
Paper: LiteFlowNet3: Resolving Correspondence Ambiguity for More Accurate Optical Flow Estimation - https://arxiv.org/abs/2007.09319
Reference code: https://github.com/twhui/LiteFlowNet3
Model names:
liteflownet3
,liteflownet3_pseudoreg
,liteflownet3s
,liteflownet3s_pseudoreg
LLA-Flow
Paper: LLA-Flow: A Lightweight Local Aggregation on Cost Volume for Optical Flow Estimation - https://arxiv.org/abs/2304.08101
Reference code: https://github.com/mansang127/LLA-Flow
Model names:
llaflow
,llaflow_raft
MaskFlownet
Paper: MaskFlownet: Asymmetric Feature Matching with Learnable Occlusion Mask - https://arxiv.org/abs/2003.10955
Reference code: https://github.com/cattaneod/MaskFlownet-Pytorch
Model names:
maskflownet
,maskflownet_s
MatchFlow
Paper: Rethinking Optical Flow from Geometric Matching Consistent Perspective - https://arxiv.org/abs/2303.08384
Reference code: https://github.com/DQiaole/MatchFlow
Model names:
matchflow
,matchflow_raft
MS-RAFT+
Paper: High-Resolution Multi-Scale RAFT - https://arxiv.org/abs/2210.16900
Reference code: https://github.com/cv-stuttgart/MS_RAFT_plus
Model names:
ms_raft+
PWCNet
Paper: PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume - https://arxiv.org/abs/1709.02371
Reference code: https://github.com/NVlabs/PWC-Net
Model names:
pwcnet
,pwcnet_nodc
RAFT
Paper: RAFT: Recurrent All-Pairs Field Transforms for Optical Flow - https://arxiv.org/abs/2003.12039
Reference code: https://github.com/princeton-vl/RAFT
Model names:
raft
,raft_small
RAPIDFlow
Paper: RAPIDFlow: Recurrent Adaptable Pyramids with Iterative Decoding for Efficient Optical Flow Estimation - https://hmorimitsu.com/publication/2024-icra-rapidflow/
Model names:
rapidflow
,rapidflow_it1
,rapidflow_it2
,rapidflow_it3
,rapidflow_it6
,rapidflow_it12
RPKNet
Paper: Recurrent Partial Kernel Network for Efficient Optical Flow Estimation - https://hmorimitsu.com/publication/2024-aaai-rpknet
Model names:
rpknet
ScopeFlow
Paper: ScopeFlow: Dynamic Scene Scoping for Optical Flow - https://arxiv.org/abs/2002.10770
Reference code: https://github.com/avirambh/ScopeFlow
Model names:
scopeflow
SCV
Paper: Learning Optical Flow from a Few Matches - https://arxiv.org/abs/2104.02166
Reference code: https://github.com/zacjiang/SCV
Model names:
scv4
,scv8
SeparableFlow
Paper: Separable Flow: Learning Motion Cost Volumes for Optical Flow Estimation - https://openaccess.thecvf.com/content/ICCV2021/papers/Zhang_Separable_Flow_Learning_Motion_Cost_Volumes_for_Optical_Flow_Estimation_ICCV_2021_paper.pdf
Reference code: https://github.com/feihuzhang/SeparableFlow
Model names:
separableflow
SKFlow
Paper: SKFlow: Learning Optical Flow with Super Kernels - https://arxiv.org/abs/2205.14623
Reference code: https://github.com/littlespray/SKFlow
Model names:
skflow
STaRFlow
Paper: STaRFlow: A SpatioTemporal Recurrent Cell for Lightweight Multi-Frame Optical Flow Estimation - https://arxiv.org/abs/2007.05481
Reference code: https://github.com/pgodet/star_flow
Model names:
starflow
VCN
Paper: Volumetric Correspondence Networks for Optical Flow - https://papers.nips.cc/paper/2019/file/bbf94b34eb32268ada57a3be5062fe7d-Paper.pdf
Reference code: https://github.com/gengshan-y/VCN
Model names:
vcn
,vcn_small
VideoFlow
Paper: Videoflow: Exploiting temporal cues for multi-frame optical flow estimation - https://arxiv.org/abs/2303.08340
Reference code: https://github.com/XiaoyuShi97/VideoFlow
Model names:
videoflow_bof
,videoflow_mof