Deep Learning-Based Image 3-D Object Detection for Autonomous Driving: Review

摘要

An accurate and robust perception system is key to understanding the driving environment of autonomous driving and robots. Autonomous driving needs 3-D information about objects, including the object’s location and pose, to understand the driving environment clearly. A camera sensor is widely used in autonomous driving because of its richness in color and texture, and low price. The major problem with the camera is the lack of 3-D information, which is necessary to understand the 3-D driving environment. In addition, the object’s scale change and occlusion make 3-D object detection more challenging. Many deep learning-based methods, such as depth estimation, have been developed to solve the lack of 3-D information. This survey presents the image 3-D object detection 3-D bounding box encoding techniques and evaluation metrics. The image-based methods are categorized based on the technique used to estimate an image’s depth information, and insights are added to each method. Then, state-of-the-art (SOTA) monocular and stereo camera-based methods are summarized. We also compare the performance of the selected 3-D object detection models and present challenges and future directions in 3-D object detection.