AIC3DOD: Advancing Indoor Class-Incremental 3D Object Detection with Point Transformer Architecture and Room Layout Constraints

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.