This paper demonstrates a novel method forLiDAR-based 3D object detection, addressing ma-jor field challenges: sparsity and occlusion. Ourapproach leverages temporal point cloud sequencesto generate frames that provide comprehensiveviews of objects from multiple angles. To addressthe challenge of generating these frames in real-time, we employ Knowledge Distillation withina Teacher-Student framework, allowing the Stu-dent model to emulate the Teacher’s advanced per-ception. We pioneered the application of weak-to-strong generalization in computer vision bytraining our Teacher model on enriched, object-complete data. In this demo, we showcase the ex-ceptional quality of labels produced by the X-RayTeacher on object-complete frames, showing ourmethod distilling its knowledge to enhance object3D detection models.