Do You Remember . . . the Future? Weak-to-Strong generalization in 3D Object Detection

Code

• github.com/sakharok13/x-ray-teacher-patching-tools
  none★ 8

Abstract

This paper demonstrates a novel method for LiDAR-based 3D object detection, addressing ma- jor field challenges: sparsity and occlusion. Our approach leverages temporal point cloud sequences to generate frames that provide comprehensive views of objects from multiple angles. To address the challenge of generating these frames in real- time, we employ Knowledge Distillation within a 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 by training our Teacher model on enriched, object- complete data. In this demo, we showcase the ex- ceptional quality of labels produced by the X-Ray Teacher on object-complete frames, showing our method distilling its knowledge to enhance object 3D detection models.

Tasks

Reproductions