Discriminative Mixture-of-Templates for Viewpoint Classification

By Chunhui Gu, Xiaofeng Ren. 2010

200 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computational Mechanics, Geology

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Citations in the corpus

Ensemble of exemplar-SVMs for object detection and beyond (2011, background): “Because most detections show good alignment to their associated exemplar, it is possible to transfer any available exemplar meta-data (segmentation, geometric structure, 3D model, etc.) directly onto the detections, which can then be used as part of overall scene understanding.”
Analyzing 3D Objects in Cluttered Images (2012, background): “Recent approaches based on view-based deformable part models include [19] [13] [10].”
6-DoF object pose from semantic keypoints (2017, methodology): “, [1], to make reliable predictions of the semantic keypoints.”
Modeling Occlusion by Discriminative AND-OR Structures (2013, methodology): “View Estimation To verify the capability of our model on view estimation, we report the mean precision in pose estimation (MPPE) on both Pascal VOC 2006 car dataset [5] and 3D Car dataset [26] following the protocol in [12] and [26], respectively.”
Detailed 3D Representations for Object Recognition and Modeling (2013, background): “…while still relying on several flat, viewpoint-specific representations, establish connections between viewpoints via homographies [62], probabilistic morphing of object parts [52], discriminative mixtures of global templates [20], or by feature tracking with integrated single-view codebooks [55].”
PVN3D: A Deep Point-Wise 3D Keypoints Voting Network for 6DoF Pose Estimation (2019, methodology): “Classical templatebased methods construct rigid templates and scan through the image to compute the best matched pose [21, 13, 17].”
PVNet: Pixel-Wise Voting Network for 6DoF Pose Estimation (2018, methodology): “Traditional methods mainly rely on template matching techniques [16, 12, 14, 42], which are sensitive to cluttered environments and appearance changes.”
Learning Category-Specific Deformable 3D Models for Object Reconstruction (2017, background): “Index Terms—Object reconstruction, 3D shape modeling, viewpoint estimation, scene understanding Ç”
DeepIM: Deep Iterative Matching for 6D Pose Estimation (2018, methodology): “In contrast, template-matching based methods are capable of handling textureless objects (Jurie and Dhome 2001; Liu et al. 2010; Gu and Ren 2010; Hinterstoisser et al. 2012a).”
Enriching object detection with 2D-3D registration and continuous viewpoint estimation (2015, background): “Several attempts have been made to extend existing detectors to estimate viewpoint along with object class detections , including the implicit shape model [25], the constellation model [24], and the deformable part model [5] [8] [28] [18] [6] [11].”
Subcategory-Aware Convolutional Neural Networks for Object Proposals and Detection (2016, unknown): “[17] performs clustering according to the viewpoint of the object to discover subcategories.”
3D Object Detection and Viewpoint Estimation with a Deformable 3D Cuboid Model (2012, background): “The most common approach to 3D detection is to discretize the viewing sphere into bins and train a 2D detector for each viewpoint [4, 5, 1, 6].”
Learning descriptors for object recognition and 3D pose estimation (2015, methodology): “[19, 11, 28] show how to build discriminative models based on these representations using SVM or boosting applied to training data.”