Lightweight Template Matching Algorithm Based on Rendering Perspective Sampling

Daizhou Wen; Xi Wang; Mingjun Ren

doi:10.3788/LOP240469

[1] Hoque S, Xu S X, Maiti A et al. Deep learning for 6D pose estimation of objects: a case study for autonomous driving[J]. Expert Systems with Applications, 223, 119838(2023).

[2] Wu X F, Qu W M, Zhang T et al. Object pose estimation with point cloud data for robot grasping[C], 1069-1074(2022).

[3] Mo L L, Ren J J, Ren M J. Microlens array-based spatial angle encoding for high-precision visual pose measurement[J]. Acta Optica Sinica, 42, 1612002(2022).

[4] Zhang H, Yang X, Chen L M et al. Automatic measurement method of cannon barrel pointing based on binocular vision[J]. Acta Optica Sinica, 43, 0212004(2023).

[5] Jing W B, Zhang J H, Feng X et al. Twist rate measurement method based on panoramic imaging for automatic tracking barrel rifling[J]. Acta Optica Sinica, 43, 2212002(2023).

[6] Lu Y W, Kourian S, Salvaggio C et al. Single image 3D vehicle pose estimation for augmented reality[C](2019).

[7] Haouchine N, Juvekar P, Nercessian M et al. Pose estimation and non-rigid registration for augmented reality during neurosurgery[J]. IEEE Transactions on Bio-Medical Engineering, 69, 1310-1317(2022).

[8] Li Z J, Stamos I. Depth-based 6DoF object pose estimation using swin transformer[C], 1185-1191(2023).

[9] Liu X Y, Wang G, Li Y, Avidan S, Brostow G, Cissé M et al. CATRE: iterative point clouds alignment for category-level object pose refinement[M]. Computer vision-ECCV 2022.Lecture notes in computer science, 13662, 499-516(2022).

[10] Shi Y F, Huang J W, Xu X et al. StablePose: learning 6D object poses from geometrically stable patches[C], 15217-15226(2021).

[11] Wang Y, Xie J, Cheng J et al. Review of object pose estimation in RGB images based on deep learning[J]. Journal of Computer Applications, 43, 2546-2555(2023).

[12] Thalhammer S, Hönig P, Weibel J B et al. Open challenges for monocular single-shot 6D object pose estimation[EB/OL]. https://arxiv.org/abs/2302.11827

[13] Chen H S, Tian W, Wang P C et al. EPro-PnP: generalized end-to-end probabilistic perspective-N-points for monocular object pose estimation[J/OL]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1-12. https://ieeexplore.ieee.org/document/10400900

[14] Thalhammer S, Patten T, Vincze M. Cope: end-to-end trainable constant runtime object pose estimation[C], 2859-2869(2023).

[15] Rad M, Lepetit V. BB8: a scalable, accurate, robust to partial occlusion method for predicting the 3D poses of challenging objects without using depth[C], 3848-3856(2017).

[16] Cui H H, Jiang T, Du K P et al. 3D imaging method for multi-view structured light measurement via deep learning pose estimation[J]. Acta Optica Sinica, 41, 1712001(2021).

[17] Do T T, Cai M, Pham T et al. Deep-6DPose: recovering 6D object pose from a single RGB image[EB/OL]. https://arxiv.org/abs/1802.10367

[18] Chen H Y, Li L T, Chen P et al. 6D pose estimation network in complex point cloud scenes[J]. Journal of Electronics & Information Technology, 44, 1591-1601(2022).

[19] Wang G, Manhardt F, Tombari F et al. GDR-net: geometry-guided direct regression network for monocular 6D object pose estimation[C], 16606-16616(2021).

[20] Zhao W Q, Zhang S B, Guan Z Y et al. 6D object pose estimation via viewpoint relation reasoning[J]. Neurocomputing, 389, 9-17(2020).

[21] Marullo G, Tanzi L, Piazzolla P et al. 6D object position estimation from 2D images: a literature review[J]. Multimedia Tools and Applications, 82, 24605-24643(2023).

[22] Zhu Y Z, Wan L L, Xu W R et al. ASPP-DF-PVNet: atrous spatial pyramid pooling and distance-filtered PVNet for occlusion resistant 6D object pose estimation[J]. Signal Processing: Image Communication, 95, 116268(2021).

[23] Nguyen V N, Hu Y L, Xiao Y et al. Templates for 3D object pose estimation revisited: generalization to new objects and robustness to occlusions[C], 6761-6770(2022).

[24] Labbé Y, Manuelli L, Mousavian A et al. MegaPose: 6D pose estimation of novel objects via render & compare[EB/OL]. http://arxiv.org/abs/2212.06870v1

[25] Wohlhart P, Lepetit V. Learning descriptors for object recognition and 3D pose estimation[C], 3109-3118(2015).

[26] Balntas V, Doumanoglou A, Sahin C et al. Pose guided RGBD feature learning for 3D object pose estimation[C], 3876-3884(2017).

[27] Hashemi N S, Aghdam R B, Ghiasi A S B et al. Template matching advances and applications in image analysis[EB/OL]. http://arxiv.org/abs/1610.07231v1

[28] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).

[29] Hu J, Shen L, Albanie S et al. Squeeze-and-excitation networks[J]. IEEE Trans Pattern Anal Mach Intell, 42, 2011-2023(2020).

[30] Howard A, Sandler M, Chen B et al. Searching for MobileNetV3[C], 1314-1324(2019).

[31] Tan M X, Le Q V. EfficientNet: rethinking model scaling for convolutional neural networks[EB/OL]. https://arxiv.org/abs/1905.11946

[32] Cao Z, Sheikh Y, Banerjee N K. Real-time scalable 6DOF pose estimation for textureless objects[C], 2441-2448(2016).

[33] Cai M, Reid I. Reconstruct locally, localize globally: a model free method for object pose estimation[C], 3150-3160(2020).

[34] Hinterstoisser S, Benhimane S, Lepetit V et al. Simultaneous recognition and homography extraction of local patches with a simple linear classifier[C], 10(2008).

[35] He K M, Fan H Q, Wu Y X et al. Momentum contrast for unsupervised visual representation learning[C], 9726-9735(2020).

[36] Hinterstoisser S, Lepetit V, Ilic S, Lee K M, Matsushita Y, Rehg J M et al. Model based training, detection and pose estimation of texture-less 3D objects in heavily cluttered scenes[M]. Computer vision - ACCV 2012.Lecture notes in computer science, 7724, 548-562(2013).

[37] Brachmann E, Krull A, Michel F, Fleet D, Pajdla T, Schiele B et al. Learning 6D object pose estimation using 3D object coordinates[M]. Computer vision - ECCV 2014.Lecture notes in computer science, 6890, 536-551(2014).

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