Single-image plane segmentation plays an important role in understanding 3D indoor scenes, including applications such as 3D indoor reconstruction. In recent years, PlaneTR, a transformer-based architecture, has achieved remarkable performance in single-image plane instance segmentation. It has garnered significant attention from researchers and remains one of the most advanced algorithms in this field. However, PlaneTR has the following two major limitations: its ineffective utilization of line segment information within images and the high number of parameters. In this study, we propose an improved version of PlaneTR, named Spatial Prompt Learning PlaneTR (SPL-PlaneTR), to address these issues. Our approach effectively balances model complexity and performance. Specifically, to more effectively leverage structural information provided by line segments, we replace the original line segment’s transformer branch with a lightweight line segment prompt module and line segment prompt adapter. Additionally, we introduce spatial queries to replace conventional position queries, enabling the network to accurately localize planes across diverse indoor scenes. The experimental results demonstrate that our model, with fewer parameters, outperforms PlaneTR on both the original and noise-corrupted ScanNet datasets. Furthermore, SPL-PlaneTR achieves superior zero-shot transfer performance on the Matterport3D, ICL-NUIM RGB-D, and 2D-3D-S datasets compared to PlaneTR. Notably, our lightweight SPL-PlaneTR also surpasses several state-of-the-art algorithms in this domain. Our code and model have been publicly available.

Plane instance segmentation from RGB-D data is a crucial research topic for many downstream tasks. However, most existing deep-learning-based methods utilize only information within the RGB bands, neglecting the important role of the depth band in plane instance segmentation. Based on EfficientSAM, a fast version of SAM, we propose a plane instance segmentation network called PlaneSAM, which can fully integrate the information of the RGB bands (spectral bands) and the D band (geometric band), thereby improving the effectiveness of plane instance segmentation in a multimodal manner. Specifically, we use a dual-complexity backbone, with primarily the simpler branch learning D-band features and primarily the more complex branch learning RGB-band features. Consequently, the backbone can effectively learn D-band feature representations even when D-band training data is limited in scale, retain the powerful RGB-band feature representations of EfficientSAM, and allow the original backbone branch to be fine-tuned for the current task. To enhance the adaptability of our PlaneSAM to the RGB-D domain, we pretrain our dual-complexity backbone using the segment anything task on large-scale RGB-D data through a self-supervised pretraining strategy based on imperfect pseudo-labels. To support the segmentation of large planes, we optimize the loss function combination ratio of EfficientSAM. In addition, Faster R-CNN is used as a plane detector, and its predicted bounding boxes are fed into our dual-complexity network as prompts, thereby enabling fully automatic plane instance segmentation. Experimental results show that the proposed PlaneSAM sets a new SOTA performance on the ScanNet dataset, and outperforms previous SOTA approaches in zero-shot transfer on the 2D-3D-S, Matterport3D, and ICL-NUIM RGB-D datasets, while only incurring a 10% increase in computational overhead compared to EfficientSAM.