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.

Labor shortages and the development of digital technology both impose requirements on the fruit industry. Modern agricultural competition has shifted from competition between products to competition between supply chains. Enhancing the digitization of production lines is crucial for gaining a competitive advantage. Strawberries, as fruits with a short shelf life, require sorting and packaging of fruits of different weights after being harvested. Estimating strawberry weight through visual technology can save time and labor costs. Common methods include methods based on feature size and learning-based methods, with the former having larger errors and the latter requiring a large amount of data. To address these issues, we propose a dataset for estimating strawberry weight, which includes strawberries with different heights and angles. Additionally, we propose a strawberry weight estimation method based on plane-constrained binary division point cloud completion. This method separates the plane point cloud and strawberry point cloud, constructs a coordinate system on the strawberry point cloud, generates an axis-aligned bounding box (AABB), and estimates the strawberry weight based on the bounding box and placement plane as constraints. Through comparison with different methods, we achieved a maximum improvement of 20.95% in prediction accuracy, demonstrating that our method provides the best estimation accuracy.

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.

Recent studies on three-dimensional(3D) point cloud data (PCD) simplification have played significant roles in computer-aided models for alleviating computational and storage burden. However, existing simplification methods are not suitable for the large-scale PCD with even billions of points. In this paper, a decomposed simplification method based on graph filter is developed to extract Haar-like feature of PCD. The new method is based on divide-and-conquer philosophy and thus effectively reduce the memory usage. In the proposed approach, point cloud is divided into several subsets according to the relationship of natural neighbor, and decomposed graph filtering with adaptive resampling rate is designed. Validation experiment is conducted on large scale PCD, which could indicate the effectiveness and feasibility of the proposed approach.

Design and construction of reinforced concrete (RC) structures are two important phases in a building construction project. Structural engineers are difficult to reject all rebar clashes in RC beam-column joints at the design phase. Construction engineers and steel fixers have to identify rebar spatial clashes and avoid rebar clashes in a manual way, which is tedious and time consuming. In this paper, an intelligent design method is urgent with the ability to avoid rebar clashes automatically. A novel artificial potential field (APF) approach is presented for the clash free layout of rebar in RC beam-column joints. Using the APF method, the layout of rebar can be regarded as the path planning of multi-agents. APF is used to generate the coordinate of the centerline of clash free rebars in a RC beam-column joint. Repulsive and attractive force can ensure a reachable and optimal solution. The simulation results showed that the proposed method is efficiency and accurate.