LPD-Net: 3D Point Cloud Learning for Large-Scale Place Recognition and Environment Analysis

By Zhe Liu, Shunbo Zhou, Chuanzhe Suo, Peng Yin, Wen Chen, Hesheng Wang, Haoang Li, Yunhui Liu. IEEE International Conference on Computer Vision, 2018

343 citations recorded by the source corpus.

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. In this paper, we develop a novel deep neural network, named LPD-Net (Large-scale Place Description Network), which can extract discriminative and generalizable global descriptors from the raw 3D point cloud. Two modules, the adaptive local feature extraction module and the graph-based neighborhood aggregation module, are proposed, which contribute to extract the local structures and reveal the spatial distribution of local features in the large-scale point cloud, with an end-to-end manner. We implement the proposed global descriptor in solving point cloud based retrieval tasks to achieve the large-scale place recognition. Comparison results show that our LPD-Net is much better than PointNetVLAD and reaches the state-of-the-art. We also compare our LPD-Net with the vision-based solutions to show the robustness of our approach to different weather and light conditions.

Subfields: Aerospace Engineering, Computational Mechanics, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/57b3b935edecaab1ab7a11ec0d68b08ce18e5990

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Sourav Garg
2. Cheng Wang
3. Weidong (Tom) Cai
4. Yang Song
5. Tobias Fischer
6. Saifullahi Aminu Bello
7. Shangshu Yu
8. Tiange Xiang

In Computational Mechanics

1. Cheng Wang
2. Weidong (Tom) Cai
3. Yang Song
4. Saifullahi Aminu Bello
5. Shangshu Yu
6. Tiange Xiang
7. Chaoda Zheng
8. Shuguang Cui

In Environmental Engineering

1. Cheng Wang
2. Saifullahi Aminu Bello
3. Shangshu Yu
4. Michael Milford

Citations in the corpus

• Review: deep learning on 3D point clouds (2020, background): “As at the time of this review, only few works [120, 121] explored deep learning on large scale 3D scene.”
• Walk in the Cloud: Learning Curves for Point Clouds Shape Analysis (2021, methodology): “Following them, recent works have extended the point-wise method to various directions, which include designing advanced convolution operations [12, 43, 38, 20], considering a wider neighborhood [15, 50, 36, 22], and the adaptive aggregation of it [6, 44, 49, 45].”
• PointASNL: Robust Point Clouds Processing Using Nonlocal Neural Networks With Adaptive Sampling (2020, background): “LPD-Net [23] further extends DGCNN on both spatial neighbors and features neighbors aggregation.”
• Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition (2021, background): “More recent approaches include ranking-loss based learning [59], novel pooling [58], contextual feature reweighting [40], large scale re-training [82], semantics-guided feature aggregation [28, 64, 75], use of 3D [53, 81, 43], additional sensors [32, 55, 22], sequences [25, 82], and image appearance”