# Sensor Fusion for Joint 3D Object Detection and Semantic Segmentation

## TL;DR

This paper enhances a LiDAR-based 3D object detector by integrating image data through sensor fusion, significantly improving long-range detection and enabling simultaneous semantic segmentation without additional image labels.

## Contribution

It introduces a straightforward sensor fusion method that boosts detection accuracy and extends capabilities to 3D semantic segmentation, all while maintaining efficiency.

## Key findings

- Achieves state-of-the-art performance on benchmark datasets.
- Improves detection accuracy at long ranges.
- Maintains low runtime despite added capabilities.

## Abstract

In this paper, we present an extension to LaserNet, an efficient and state-of-the-art LiDAR based 3D object detector. We propose a method for fusing image data with the LiDAR data and show that this sensor fusion method improves the detection performance of the model especially at long ranges. The addition of image data is straightforward and does not require image labels. Furthermore, we expand the capabilities of the model to perform 3D semantic segmentation in addition to 3D object detection. On a large benchmark dataset, we demonstrate our approach achieves state-of-the-art performance on both object detection and semantic segmentation while maintaining a low runtime.