MonoLite3D: Lightweight 3D Object Properties Estimation
This addresses the need for lightweight 3D perception in resource-constrained environments like embedded devices for self-driving vehicles, but it is incremental as it builds on existing monocular methods.
The paper tackles the problem of estimating 3D object properties like dimensions and orientation from monocular images for self-driving vehicles, achieving scores of 82.27% on the moderate class and 69.81% on the hard class on the KITTI dataset while meeting real-time requirements.
Reliable perception of the environment plays a crucial role in enabling efficient self-driving vehicles. Therefore, the perception system necessitates the acquisition of comprehensive 3D data regarding the surrounding objects within a specific time constrain, including their dimensions, spatial location and orientation. Deep learning has gained significant popularity in perception systems, enabling the conversion of image features captured by a camera into meaningful semantic information. This research paper introduces MonoLite3D network, an embedded-device friendly lightweight deep learning methodology designed for hardware environments with limited resources. MonoLite3D network is a cutting-edge technique that focuses on estimating multiple properties of 3D objects, encompassing their dimensions and spatial orientation, solely from monocular images. This approach is specifically designed to meet the requirements of resource-constrained environments, making it highly suitable for deployment on devices with limited computational capabilities. The experimental results validate the accuracy and efficiency of the proposed approach on the orientation benchmark of the KITTI dataset. It achieves an impressive score of 82.27% on the moderate class and 69.81% on the hard class, while still meeting the real-time requirements.