Pointnetvlad: Deep Point Cloud Based Retrieval For Large-scale Place Recognition
2018 · Mikaela Angelina Uy, Gim Hee Lee
Abstract
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 th
Authors
(none)
Tags
Stats
Related papers
- Efficient 3D Point Cloud Feature Learning For Large-scale Place Recognition (2021)14.73
- Lpd-net: 3D Point Cloud Learning For Large-scale Place Recognition And Environment Analysis (2018)18.40
- Multires-netvlad: Augmenting Place Recognition Training With Low-resolution Imagery (2022)16.01
- Logg3d-net: Locally Guided Global Descriptor Learning For 3D Place Recognition (2021)19.02
- City-scale Visual Place Recognition With Deep Local Features Based On Multi-scale Ordered VLAD Pooling (2020)1.69
- PCAN: 3D Attention Map Learning Using Contextual Information For Point Cloud Based Retrieval (2019)17.42
- Deeppoint3d: Learning Discriminative Local Descriptors Using Deep Metric Learning On 3D Point Clouds (2019)9.59
- DH3D: Deep Hierarchical 3D Descriptors For Robust Large-scale 6dof Relocalization (2020)14.76