SOA: Seed point offset attention for indoor 3D object detection in point clouds

Abstract

Three-dimensional object detection plays a pivotal role in scene understanding and holds significant importance in various indoor perception applications. Traditional methods based on Hough voting are susceptible to interference from background points or neighboring objects when casting votes for the target’s center from each seed point. Moreover, fixed-size set abstraction modules may result in the loss of structural information for large objects. To address these challenges, this paper proposes a three-dimensional object detection model based on seed point offset attention. The objective of this model is to enhance the model’s resilience to voting noise interference and alleviate feature loss for large-scale objects. Specifically, a seed point offset tensor is first defined, and then the offset tensor self-attention network is employed to learn the weights between votes, thereby establishing a correlation between the voting semantic features and the object structural information. Furthermore, an object surface perception module is introduced, which incorporates detailed features of local object surfaces into global feature representations through vote backtracking and surface mapping. Experimental results indicate that the model achieved excellent performance on the ScanNet-V2 (mAP@0.5, 60.3%) and SUN RGB-D (mAP@0.5, 64.0%) datasets, respectively improving by 2.6% (mAP@0.5) and 5.4% (mAP@0.5) compared to VoteNet.