In this paper, we propose LPCG (LiDAR point cloud guided monocular 3D object detection), which is a general framework for guiding the training of monocular 3D detectors with LiDAR point clouds.
Therefore, it is critical to learn an apparel-invariant person representation under cases like cloth changing or several persons wearing similar clothes.
Most deep-learning-based image classification methods assume that all samples are generated under an independent and identically distributed (IID) setting.
Moreover, based on the PACF module, we propose a 3D multi-sensor multi-task network called Pointcloud-Image RCNN(PI-RCNN as brief), which handles the image segmentation and 3D object detection tasks.
Model fine-tuning is a widely used transfer learning approach in person Re-identification (ReID) applications, which fine-tuning a pre-trained feature extraction model into the target scenario instead of training a model from scratch.