PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation

By Yang Zhang, Zixiang Zhou, Philip David, Xiangyu Yue, Zerong Xi, Hassan Foroosh. Computer Vision and Pattern Recognition, 2020

610 citations recorded by the source corpus.

The requirement of fine-grained perception by autonomous driving systems has resulted in recently increased research in the online semantic segmentation of single-scan LiDAR. Emerging datasets and technological advancements have enabled researchers to benchmark this problem and improve the applicable semantic segmentation algorithms. Still, online semantic segmentation of LiDAR scans in autonomous driving applications remains challenging due to three reasons: (1) the need for near-real-time latency with limited hardware, (2) points are distributed unevenly across space, and (3) an increasing number of more fine-grained semantic classes. The combination of the aforementioned challenges motivates us to propose a new LiDAR-specific, KNN-free segmentation algorithm - PolarNet. Instead of using common spherical or bird's-eye-view projection, our polar bird's-eye-view representation balances the points per grid and thus indirectly redistributes the network's attention over the long-tailed points distribution over the radial axis in polar coordination. We find that our encoding scheme greatly increases the mIoU in three drastically different real urban LiDAR single-scan segmentation datasets while retaining ultra low latency and near real-time throughput.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/5f8675ebf66d0867b626bdd6da6a8b209f84a7a2

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. Xinge Zhu
2. Tai Wang
3. Dahua Lin
4. Hongsheng Li
5. Guan Huang
6. Junjie Huang
7. Jian‐Yuan Sun

In Computer Vision and Pattern Recognition

1. Hongsheng Li
2. Dahua Lin
3. Xinge Zhu
4. Tai Wang
5. Xiaogang Wang
6. Shiliang Pu
7. Luc Van Gool
8. Dacheng Tao

In Environmental Engineering

1. Dahua Lin
2. Tai Wang
3. Xinge Zhu
4. Hongsheng Li

Citations in the corpus

• Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution (2020, neutral): “8 PolarNet [76] 13.”
• RPVNet: A Deep and Efficient Range-Point-Voxel Fusion Network for LiDAR Point Cloud Segmentation (2021, background): “As shown in Table 2, our RPVNet achieves better performance when compared to previous competitive methods, including RangeNet++ [23], PolarNet [41], Salsanext [12], AMVNet [20] and Cylinder3D [44], and Salsanext perform the post-processing.”
• Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation (2020, methodology): “PolarNet [51] follows the bird’s-eye-view projection, which projects point cloud data into bird’s-eye-view representation under the polar coordinates.”