Quantization to accelerate inference in multimodal 3D object detection

Abstract

The Label-Diffusion-LIDAR-Segmentation (LDLS) algorithm uses multi-modal data for enhanced inference of environmental categories. The algorithm segments the Red-Green-Blue (RGB) channels and maps the results to the LIDAR point cloud using matrix calculations to reduce noise. Recent research has developed custom optimization techniques using quantization to accelerate the 3D object detection using LDLS in robotic systems. These optimizations achieve a 3x speedup over the original algorithm, making it possible to deploy the algorithm in real-world applications. The optimizations include quantization for the segmentation inference as well as matrix optimizations for the label diffusion. We will present our results, compare them with the baseline, and discuss their significance in achieving real-time object detection in resource-constrained environments.