Rethinking 3D LiDAR Point Cloud Segmentation
This addresses the problem of slow and inaccurate semantic segmentation for outdoor LiDAR point clouds, which is incremental as it adapts existing methods rather than introducing a new paradigm.
The paper tackles the inefficiency and poor performance of point-based semantic segmentation methods on outdoor LiDAR data by reformulating 3D point-based operations to work in projection space, resulting in 300-400 times faster speeds and higher accuracy.
Many point-based semantic segmentation methods have been designed for indoor scenarios, but they struggle if they are applied to point clouds that are captured by a LiDAR sensor in an outdoor environment. In order to make these methods more efficient and robust such that they can handle LiDAR data, we introduce the general concept of reformulating 3D point-based operations such that they can operate in the projection space. While we show by means of three point-based methods that the reformulated versions are between 300 and 400 times faster and achieve a higher accuracy, we furthermore demonstrate that the concept of reformulating 3D point-based operations allows to design new architectures that unify the benefits of point-based and image-based methods. As an example, we introduce a network that integrates reformulated 3D point-based operations into a 2D encoder-decoder architecture that fuses the information from different 2D scales. We evaluate the approach on four challenging datasets for semantic LiDAR point cloud segmentation and show that leveraging reformulated 3D point-based operations with 2D image-based operations achieves very good results for all four datasets.