# Deep Semantic Classification for 3D LiDAR Data

## TL;DR

This paper presents a deep learning-based method for classifying 3D LiDAR points into non-movable, movable, and dynamic categories, enhancing autonomous robot perception by integrating semantic and motion cues.

## Contribution

It introduces a novel approach combining neural network-based semantic classification with motion estimation within a Bayes filter framework for improved 3D LiDAR data understanding.

## Key findings

- Achieves competitive results on standard benchmarks.
- Improves point classification accuracy by combining semantic and motion cues.
- Demonstrates effective distinction between static, movable, and dynamic objects.

## Abstract

Robots are expected to operate autonomously in dynamic environments. Understanding the underlying dynamic characteristics of objects is a key enabler for achieving this goal. In this paper, we propose a method for pointwise semantic classification of 3D LiDAR data into three classes: non-movable, movable and dynamic. We concentrate on understanding these specific semantics because they characterize important information required for an autonomous system. Non-movable points in the scene belong to unchanging segments of the environment, whereas the remaining classes corresponds to the changing parts of the scene. The difference between the movable and dynamic class is their motion state. The dynamic points can be perceived as moving, whereas movable objects can move, but are perceived as static. To learn the distinction between movable and non-movable points in the environment, we introduce an approach based on deep neural network and for detecting the dynamic points, we estimate pointwise motion. We propose a Bayes filter framework for combining the learned semantic cues with the motion cues to infer the required semantic classification. In extensive experiments, we compare our approach with other methods on a standard benchmark dataset and report competitive results in comparison to the existing state-of-the-art. Furthermore, we show an improvement in the classification of points by combining the semantic cues retrieved from the neural network with the motion cues.