RGB-D Cube R-CNN: 3D Object Detection with Selective Modality Dropout

In this paper we create an RGB-D 3D object detector targeted at indoor robotics use cases where one modality may be unavailable due to a specific sensor setup or a sensor failure. We incorporate RGB and depth fusion into the recent Cube R-CNN framework with support for selective modality dropout. To train this model we augment the Omni3DIN dataset with depth information leading to a diverse dataset for 3D object detection in indoor scenes. In order to leverage strong pretrained networks we investigate the viability of Transformer-based backbones (Swin ViT) as an alternative to the currently popular CNN-based DLA backbone. We show that these Transformer-based image models work well based on our early-fusion approach and propose a modality dropout scheme to avoid the disregard of any modality during training facilitating selective modality dropout during inference. In extensive experiments our proposed RGB-D Cube R-CNN outperforms an RGB-only Cube R-CNN baseline by a significant margin on the task of indoor object detection. Additionally we observe a slight performance boost from the RGB-D training when inferring on only one modality which could for example be valuable in robotics applications with a reduced or unreliable sensor set.