2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2017
DOI: 10.1109/iros.2017.8206488
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Deep learning of directional truncated signed distance function for robust 3D object recognition

Abstract: In this paper, we develop a novel 3D object recognition algorithm to perform detection and pose estimation jointly. We focus on analyzing the advantages of the 3D point cloud relative to the RGB-D image and try to eliminate the unpredictability of output values that inevitably occurs in regression tasks. To achieve this, we first adopt the Truncated Signed Distance Function (TSDF) to encode the point cloud and extract low compact discriminative feature via unsupervised deep learning network. This approach can … Show more

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Cited by 8 publications
(11 citation statements)
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“…4.1. Results on the LC-HF dataset [25,27] This dataset [25,27] The statistic recognition results are shown in Table 1 , where the overall average F1-score of our method is 93.9%, in comparison with LineMod (74.0%) [21] , LC-HF (65.1%) [25] , ConvAE (74.7%) [27] , VoxelAE (76.8%) [28] and SSD-6D (88.5%) [29] respectively. Here, [29] counts a detection to be correct when the IoU score of a predicted bounding box with the groundtruth box is higher than 0.5.…”
Section: Methodsmentioning
confidence: 99%
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“…4.1. Results on the LC-HF dataset [25,27] This dataset [25,27] The statistic recognition results are shown in Table 1 , where the overall average F1-score of our method is 93.9%, in comparison with LineMod (74.0%) [21] , LC-HF (65.1%) [25] , ConvAE (74.7%) [27] , VoxelAE (76.8%) [28] and SSD-6D (88.5%) [29] respectively. Here, [29] counts a detection to be correct when the IoU score of a predicted bounding box with the groundtruth box is higher than 0.5.…”
Section: Methodsmentioning
confidence: 99%
“…Instead, W. Kehl et al [27] train a Convolution Autoencoder to extract patch features and estimate 6-DOF pose based on K-nn search, which gives better performance. Liu et al [28] present a 3D Voxel Autoencoder by converting the point clouds into voxel grids for fully using the 3D spatial structure information.…”
Section: Related Workmentioning
confidence: 99%
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