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Register a new userAttentive Rotation Invariant Convolution for Point Cloud-based Large Scale Place Recognition
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Added by
Zhaoxin Fan
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Autonomous Driving and Simultaneous Localization and Mapping(SLAM) are becoming increasingly important in real world, where point cloud-based large scale place recognition is the spike of them. Previous place recognition methods have achieved acceptable performances by regarding the task as a point cloud retrieval problem. However, all of them are suffered from a common defect: they cant handle the situation when the point clouds are rotated, which is common, e.g, when viewpoints or motorcycle types are changed. To tackle this issue, we propose an Attentive Rotation Invariant Convolution (ARIConv) in this paper. The ARIConv adopts three kind of Rotation Invariant Features (RIFs): Spherical Signals (SS), Individual-Local Rotation Invariant Features (ILRIF) and Group-Local Rotation Invariant features (GLRIF) in its structure to learn rotation invariant convolutional kernels, which are robust for learning rotation invariant point cloud features. Whats more, to highlight pivotal RIFs, we inject an attentive module in ARIConv to give different RIFs different importance when learning kernels. Finally, utilizing ARIConv, we build a DenseNet-like network architecture to learn rotation-insensitive global descriptors used for retrieving. We experimentally demonstrate that our model can achieve state-of-the-art performance on large scale place recognition task when the point cloud scans are rotated and can achieve comparable results with most of existing methods on the original non-rotated datasets.
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Unlike its image based counterpart, point cloud based retrieval for place recognition has remained as an unexplored and unsolved problem. This is largely due to the difficulty in extracting local feature descriptors from a point cloud that can subsequently be encoded into a global descriptor for the retrieval task. In this paper, we propose the PointNetVLAD where we leverage on the recent success of deep networks to solve point cloud based retrieval for place recognition. Specifically, our PointNetVLAD is a combination/modification of the existing PointNet and NetVLAD, which allows end-to-end training and inference to extract the global descriptor from a given 3D point cloud. Furthermore, we propose the lazy triplet and quadruplet loss functions that can achieve more discriminative and generalizable global descriptors to tackle the retrieval task. We create benchmark datasets for point cloud based retrieval for place recognition, and the experimental results on these datasets show the feasibility of our PointNetVLAD. Our code and the link for the benchmark dataset downloads are available in our project website. http://github.com/mikacuy/pointnetvlad/
In the field of large-scale SLAM for autonomous driving and mobile robotics, 3D point cloud based place recognition has aroused significant research interest due to its robustness to changing environments with drastic daytime and weather variance. However, it is time-consuming and effort-costly to obtain high-quality point cloud data for place recognition model training and ground truth for registration in the real world. To this end, a novel registration-aided 3D domain adaptation network for point cloud based place recognition is proposed. A structure-aware registration network is introduced to help to learn features with geometric information and a 6-DoFs pose between two point clouds with partial overlap can be estimated. The model is trained through a synthetic virtual LiDAR dataset through GTA-V with diverse weather and daytime conditions and domain adaptation is implemented to the real-world domain by aligning the global features. Our results outperform state-of-the-art 3D place recognition baselines or achieve comparable on the real-world Oxford RobotCar dataset with the visualization of registration on the virtual dataset.
We propose a local-to-global representation learning algorithm for 3D point cloud data, which is appropriate to handle various geometric transformations, especially rotation, without explicit data augmentation with respect to the transformations. Our model takes advantage of multi-level abstraction based on graph convolutional neural networks, which constructs a descriptor hierarchy to encode rotation-invariant shape information of an input object in a bottom-up manner. The descriptors in each level are obtained from a neural network based on a graph via stochastic sampling of 3D points, which is effective in making the learned representations robust to the variations of input data. The proposed algorithm presents the state-of-the-art performance on the rotation-augmented 3D object recognition and segmentation benchmarks, and we further analyze its characteristics through comprehensive ablative experiments.
Sign language is a gesture based symbolic communication medium among speech and hearing impaired people. It also serves as a communication bridge between non-impaired population and impaired population. Unfortunately, in most situations a non-impaired person is not well conversant in such symbolic languages which restricts natural information flow between these two categories of population. Therefore, an automated translation mechanism can be greatly useful that can seamlessly translate sign language into natural language. In this paper, we attempt to perform recognition on 30 basic Indian sign gestures. Gestures are represented as temporal sequences of 3D depth maps each consisting of 3D coordinates of 20 body joints. A recurrent neural network (RNN) is employed as classifier. To improve performance of the classifier, we use geometric transformation for alignment correction of depth frames. In our experiments the model achieves 84.81% accuracy.
Many recent works show that a spatial manipulation module could boost the performances of deep neural networks (DNNs) for 3D point cloud analysis. In this paper, we aim to provide an insight into spatial manipulation modules. Firstly, we find that the smaller the rotational degree of freedom (RDF) of objects is, the more easily these objects are handled by these DNNs. Then, we investigate the effect of the popular T-Net module and find that it could not reduce the RDF of objects. Motivated by the above two issues, we propose a rotation transformation network for point cloud analysis, called RTN, which could reduce the RDF of input 3D objects to 0. The RTN could be seamlessly inserted into many existing DNNs for point cloud analysis. Extensive experimental results on 3D point cloud classification and segmentation tasks demonstrate that the proposed RTN could improve the performances of several state-of-the-art methods significantly.
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