2D-3D cross modal retrieval (CMR) aims to retrieve query image matching points from a 3D reference map. Existing classical CMR datasets and methods commonly support database-based retrieval only, i.e., the point cloud retrieval results are fixed-scale geometric surfaces. The failure to consider geometric regions and information scales fundamentally limits the practical deployment of CMR in engineering systems that require dynamic spatial reasoning, such as autonomous navigation or three-dimensional industrial measurement. In this article, we introduce a new benchmark called cross modal regional retrieval, which extends the classic CMR to allow the free retrieval of associated regions within the point cloud from images. Toward this, a multiview training paradigm is proposed in the training phase, which enables the model to identify occluded points in the region based on a single view. Autoencoders are utilized to learn the mapping of fusion features from a single view to multiple views. We also convert the image retrieval task within the scene cloud into a point classification task in the image to implement global free retrieval. The information fusion and guidance provided by the global point cloud enhances the capability of image cross-modal retrieval. To match the input patterns of the model, we propose a method for constructing datasets from three benchmark sources. Extensive experiments demonstrate that our method achieves state-of-the-art performance compared to existing methods for 2D-3D cross modal regional retrieval.

House layout plays a crucial role in housing planning and design. In recent years, automated generation of house layouts has gained significant attention. The objective is to automatically generate floorplans that meet specific requirements under given constraints. In this paper, we present an extension and improvement of the existing diffusion model, focusing on the generation of housing layouts in more complex constrained scenarios. Firstly, we introduce the consideration of relative room area ranking as a new problem scenario. Secondly, we propose an indirect encoding approach that represents the ranking relationship of room relative areas as a directed graph to address potential issues arising from embedding ranking features. Finally, we introduce a corresponding attention module to capture the newly added constraint relationships. Moreover, we evaluate the proposed approach using a range of metrics and the RPLAN dataset. Our proposed method demonstrates improved accuracy in considering variations in room sizes and provides more reasonable layout. The obtained results also indicate that our enhanced approach exhibits better compatibility and Spearman correlation for relative room area ranking compared to the state-of-the-art methods.