Point Cloud Retrieval
14 papers with code • 1 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Point Cloud Retrieval
| Trend | Dataset | Best Model | Paper | Code | Compare |
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Most implemented papers
PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognition
mikacuy/pointnetvlad
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CVPR 2018
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.
LPD-Net: 3D Point Cloud Learning for Large-Scale Place Recognition and Environment Analysis
Point cloud based place recognition is still an open issue due to the difficulty in extracting local features from the raw 3D point cloud and generating the global descriptor, and it's even harder in the large-scale dynamic environments.
PCAN: 3D Attention Map Learning Using Contextual Information for Point Cloud Based Retrieval
Point cloud based retrieval for place recognition is an emerging problem in vision field.
DH3D: Deep Hierarchical 3D Descriptors for Robust Large-Scale 6DoF Relocalization
We generate the global descriptor by directly aggregating the learned local descriptors with an effective attention mechanism.
MinkLoc3D: Point Cloud Based Large-Scale Place Recognition
jac99/MinkLoc3D
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Thus, state-of-the-art methods enhance vanilla PointNet architecture by adding different mechanism to capture local contextual information, such as graph convolutional networks or using hand-crafted features.
SOE-Net: A Self-Attention and Orientation Encoding Network for Point Cloud based Place Recognition
We tackle the problem of place recognition from point cloud data and introduce a self-attention and orientation encoding network (SOE-Net) that fully explores the relationship between points and incorporates long-range context into point-wise local descriptors.
Pyramid Point Cloud Transformer for Large-Scale Place Recognition
In order to obtain discriminative global descriptors, we construct a pyramid VLAD module to aggregate the multi-scale feature maps of point clouds into the global descriptors.
Efficient 3D Point Cloud Feature Learning for Large-Scale Place Recognition
fpthink/EPC-Net
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In this paper, we develop an efficient point cloud learning network (EPC-Net) to form a global descriptor for visual place recognition, which can obtain good performance and reduce computation memory and inference time.
NDT-Transformer: Large-Scale 3D Point Cloud Localisation using the Normal Distribution Transform Representation
dachengxiaocheng/NDT-Transformer
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Benefiting from the NDT representation and NDT-Transformer network, the learned global descriptors are enriched with both geometrical and contextual information.
MinkLoc++: Lidar and Monocular Image Fusion for Place Recognition
We also identify dominating modality problem when training a multimodal descriptor.
