  3D object detection has achieved significant performance in many fields,
e.g., robotics system, autonomous driving, and augmented reality. However, most
existing methods could cause catastrophic forgetting of old classes when
performing on the class-incremental scenarios. Meanwhile, the current
class-incremental 3D object detection methods neglect the relationships between
the object localization information and category semantic information and
assume all the knowledge of old model is reliable. To address the above
challenge, we present a novel Incremental 3D Object Detection framework with
the guidance of prompting, i.e., I3DOD. Specifically, we propose a task-shared
prompts mechanism to learn the matching relationships between the object
localization information and category semantic information. After training on
the current task, these prompts will be stored in our prompt pool, and perform
the relationship of old classes in the next task. Moreover, we design a
reliable distillation strategy to transfer knowledge from two aspects: a
reliable dynamic distillation is developed to filter out the negative knowledge
and transfer the reliable 3D knowledge to new detection model; the relation
feature is proposed to capture the responses relation in feature space and
protect plasticity of the model when learning novel 3D classes. To the end, we
conduct comprehensive experiments on two benchmark datasets and our method
outperforms the state-of-the-art object detection methods by 0.6% - 2.7% in
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