Over the recent years, there has been a growing interest in class-incremental 3D object detection based on point clouds. However, the current state-of-the-art (SOTA) methods still fall short of practical adoption, mainly due to two key observations. Firstly, existing SOTA methods are limited by the capability of feature representation from the object detection model. Secondly, these methods overlook the importance of incorporating prior information or geometry constraints, which are crucial elements for 3D point cloud tasks. In this study, we strive to enhance the performance of class-incremental 3D object detection for indoor scenes by proposing AIC3DOD - Advancing Indoor Classincremental 3D Object Detection using the point transformer architecture with room layout constraints. Our approach employs a transformer architecture in our detection model and optimizes the class incremental step in the transformer architecture. Besides, AIC3DOD incorporates additional prior information, namely room layout, to impose physical constraints on detected objects, thereby enhancing overall object detection performance. Extensive experimental results on the ScanNet dataset demonstrate the effectiveness of our approach, showcasing our superior performance compared to other SOTA methods in the class-incremental 3D object detection task.

Deep learning approaches have demonstrated high effectiveness in 3D object detection tasks. However, they often suffer from a notable drop in performance on the previously trained classes when learning new classes incrementally without revisiting the old data. This is the "catastrophic forgetting" phenomenon which impedes 3D object detection in real-world scenarios, where intelligent machines must continuously learn to detect previously unseen categories. Furthermore, frequent co-occurrences of old and new classes in scenes exacerbate catastrophic forgetting and cause model confusion. To address these challenges, we propose a novel static-dynamic co-teaching approach. Our framework involves a student model and two teacher models: a static teacher with fixed weights which imparts preserved old knowledge to the student, and a dynamic teacher with continuously updated weights which transfers underlying knowledge from new data to the student. To mitigate the issue of co-occurrence, we generate pseudo labels for base (i.e. old) classes from both static and dynamic sources during incremental learning. Additionally, to mitigate the negative impact of varying occurrence frequencies of classes on fixed thresholding during the selection of pseudo labels, we calibrate the probabilities of base classes to attain more balanced class probabilities. Moreover, our static-dynamic co-teaching framework is backbone-agnostic, making it compatible with different detection architectures. We demonstrate its backbone-agnostic nature by adapting three representative 3D object detectors: VoteNet, 3DETR and CAGroup3D. Extensive experiments showcase the superior performance of our proposed method compared to baseline approaches across indoor and outdoor benchmark datasets and applicability with different backbone models.