A Global Hypotheses Verification Method for 3D Object Recognition

By Aitor Aldoma, Federico Tombari, Luigi Di Stefano, Markus Vincze. 2012

187 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

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Citations in the corpus

RGB-D datasets using microsoft kinect or similar sensors: a survey (2016, methodology): “The curves under the images are the accelerometer data at 50 Hz of devices attached to the knife, the mixing spoon, the small spoon, the peeler, the glass, the oil bottle, and the pepper dispenser mentioned datasets above, another 26 extra RGB-D datasets for different applications are also added int”
ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes (2017, background): “A large number of RGB-D datasets have been captured and made publicly available for training and benchmarking [56, 34, 50, 65, 79, 83, 74, 4, 58, 81, 15, 55, 1, 68, 30, 51, 21, 48, 43, 92, 80, 61, 72, 93, 36, 16, 35, 57, 40, 29, 70, 52, 45, 95, 75, 9, 33, 85, 71, 32, 3, 10, 78, 2].”
Recovering 6D Object Pose and Predicting Next-Best-View in the Crowd (2015, methodology): “[1] proposed a cost function-based optimization to increase true positive detections.”
SHOT: Unique signatures of histograms for surface and texture description (2014, background): “This is the case of [47], where the authors report their choice of SHOT as 3D descriptor because of the superior performance with respect to the other considered approaches, as well as of [48], whose proposed SHOTbased 3D pipeline was able to solve the benchmark dataset introduced in [18].”
3D Object Recognition in Cluttered Scenes with Local Surface Features: A Survey (2014, methodology): “One limitation of this approach is that objects with occlusions larger than 1-ta cannot be recognized [11].”
T-LESS: An RGB-D Dataset for 6D Pose Estimation of Texture-Less Objects (2017, background): “[2] provide 3D mesh models without color information of 35 household objects that are both textured and texture-less and are often symmetric and mutually similar in shape and size.”
Deep Learning Advances in Computer Vision with 3D Data (2017, methodology): “2014; Aldoma et al. 2012b]. Local descriptors-based methods (e.g., Aldoma et al. [2012a], Tombari and Di Stefano [2012], and Guo et al. [2013]) exploit local detectors and descriptors in order to extract a rich representation at first from all available object models and afterward, from the examined”
Category Modeling from Just a Single Labeling: Use Depth Information to Guide the Learning of 2D Models (2013, background): “Recently, RGBD images made object detection much easier [14, 15, 16], and even the structure discovery [17] or segmentation of indoor environments [18, 19] produced object-level results.”
Learning to Detect Good 3D Keypoints (2017, methodology): “13, the relative ordering of hand-crafted detectors does change: on the one hand, the baseline uniform sampling approach performs better than KPQ, ISS, and Harris3D; on the other hand, NARF, whose performance was quite unsatisfactory on previous datasets, turns out the best hand-crafted detector onK”
A Comprehensive Performance Evaluation of 3D Local Feature Descriptors (2015, methodology): “They are available in the Point Cloud Library (PCL) Version 1.7.1 (Rusu and Cousins 2011; Aldoma et al. 2012a), while the others were implemented in Matlab 2011b.”
Recognizing Objects in 3D Point Clouds with Multi-Scale Local Features (2014, methodology): “The UWA dataset is currently regarded as the most popular benchmark for 3D object recognition [4,10,11,13,19,27,32].”
SUN RGB-D: A RGB-D scene understanding benchmark suite (2015, unknown): “There are many existing RGB-D datasets available [54] [47] [1] [25] [44] [60] [49] [45] [29] [66] [57] [52] [46] [16] [21] [73] [35] [67] [3] [41] [10] [63] [42] [64] [65] [48] [12] [33] [8] [50] [26] [6].”