Objects in remote sensing images often exhibit arbitrary orientations and significant scale variations, posing substantial challenges for accurate detection. Mainstream approaches typically rely on regressing oriented bounding box (OBB) based on predefined anchor boxes. However, issues such as angle discontinuity during regression significantly degrade performance. To enhance flexibility, recent works have proposed using a set of adaptive points instead of traditional bounding boxes, particularly within the RepPoints framework, which models object geometry and pose via point-based representations. In this work, we revisit RepPoints-based methods and identify critical issues, including partial point usage problem and training-testing inconsistency problem. To address these limitations, we propose a novel concept of the deformable bounding box (DBB) and design an anchor-free detector, DBB-Det, built upon this representation. Furthermore, we introduce a Normalized Spatial Constraint Loss to ensure scale-invariant penalization of outlier points, effectively balancing detection across varied object sizes. Extensive experiments on three challenging remote sensing benchmarks including DOTA, DIOR-R, and HRSC2016, demonstrate that our approach achieves superior accuracy and robustness. Moreover, the proposed deformable bounding box design is easily transferable to other RepPoints-based detectors.