Performance of histogram descriptors for the classification of 3D laser range data in urban environments

By Jens Behley, Volker Steinhage, Armin Bernd Cremers. 2012

111 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Environmental Engineering, Geology

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Citations in the corpus

3D fully convolutional network for vehicle detection in point cloud (2016, background): “A comparison of these tranditional handcrafted features can be found in [2].”
Vehicle Detection from 3D Lidar Using Fully Convolutional Network (2016, background): “A comparison of features can be found in [1].”
FusionNet: 3D Object Classification Using Multiple Data Representations (2016, neutral): “Classification of 3D objects has been proposed using hand-crafted features along with a machine learning classifier in [14], [25] and [1].”
Performance of global descriptors for velodyne-based urban object recognition (2014, methodology): “For the sake of improving the performance of recognition, Conditional Random Field (CRF) [18], [19] , [21], [22] are often used as classifiers.”
SPNet: Deep 3D Object Classification and Retrieval using Stereographic Projection (2018, methodology): “Point cloud-based methods: While previous works often combine hand-crafted features or descriptors with a machine learning classiﬁer [11,32,4,9], the point cloud-based methods operate directly on point clouds in an end-to-end manner.”
VoxNet: A 3D Convolutional Neural Network for real-time object recognition (2015, methodology): “Most of this work uses a pipeline combining various hand-crafted features or descriptors with a machine learning classifier ([10], [11], [12], [13]).”
Semantic Instance Annotation of Street Scenes by 3D to 2D Label Transfer (2015, background): “For street scenes, less annotated data is available [3, 29, 30, 33, 43].”
A2D2: Audi Autonomous Driving Dataset (2020, unknown): “[9, 10, 11, 12, 13].”
Laser-based segment classification using a mixture of bag-of-words (2013, methodology): “We calculated spin images [16], since these showed good results for point-wise classification in earlier experiments [23] and are relatively fast to compute.”
SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences (2019, methodology): “Other publicly available datasets, like Paris-Lille-3D [47] or Wachtberg [6], also use such sensors, but only provide the aggregated point cloud of the whole acquired sequence or some individual scans of the whole sequence, respectively.”
KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3D (2021, background): “Outdoor Datasets: A number of outdoor datasets of driving scenes have been released in the literature [8], [9], [13], [18], [25], [34], [36], [45], [52], [56], [62], [65], [70], [80], [84], [96], [99], [103], [111], [119].”
SHOT: Unique signatures of histograms for surface and texture description (2014, background): “[52] show that SHOT yields good performance in the context of classification of urban data acquired from LIDARs, resulting overall one of the two best performers.”
Learning to Detect Good 3D Keypoints (2017, background): “…of comparative evaluations addressing object recognition (Alexandre 2012; Aldoma et al. 2012a), 3D object classification and retrieval (Proença et al. 2013; Wohlkinger et al. 2012), semantic segmentation of point clouds (Behley et al. 2012), localization ofmedical landmarks (Sukno et al. 2012).”