SDA-Net: A Global Feature Point Cloud Completion Network Based on Serialization and Dual Attention.

Point cloud completion aims to reconstruct the complete 3D shape from incomplete point clouds, and it is crucial for tasks such as 3D object detection and segmentation. Despite the continuous advances in point cloud analysis techniques, feature extraction methods are still confronted with apparent limitations. The sparse sampling of point clouds, used as inputs in most methods, often results in a certain loss of global structure information. Meanwhile, traditional local feature extraction methods usually struggle to capture the intricate geometric details. To overcome these drawbacks, we introduce PointCFormer, a transformer framework optimized for robust global retention and precise local detail capture in point cloud completion. This framework embraces several key advantages. First, we propose a relation-based local feature extraction method to perceive local delicate geometry characteristics. This approach establishes a fine-grained relationship metric between the target point and its k-nearest neighbors, quantifying each neighboring point's contribution to the target point's local features. Secondly, we introduce a progressive feature extractor that integrates our local feature perception method with self-attention. Starting with a denser sampling of points as input, it iteratively queries long-distance global dependencies and local neighborhood relationships. This extractor maintains enhanced global structure and refined local details, without generating substantial computational overhead. Additionally, we develop a correction module after generating point proxies in the latent space to reintroduce denser information from the input points, enhancing the representation capability of the point proxies. PointCFormer demonstrates state-of-the-art performance on several widely used benchmarks.

Reconstructing the incomplete point cloud into the complete and uniform one is a fundamental task in 3D point cloud processing. Some existing studies have explored the use of deep learning networks and representation learning to implement point cloud completion, but the completed shapes still appear unrealistic and non-uniform. To address this issue, we propose the idea of simulating the process of point cloud completion with local-to-global reasoning (LGR). Motivated to achieve the fine LGR, a novel mutual information (MI) maximization-based similarity operation is proposed, which realizes the reconstruction from incomplete point cloud to complete one by maximizing MI between global features and the prior features from the same 3D object. We adopt the Jenson-Shannon MI estimator to maximize the MI between global features and the priors in specific dimensions, which effectively increases the similarity of global and prior features. Compared with other existing works and similarity operations, the superior results indicate the efficacy of the proposed method and its advantages over existing ones in both the synthetic and real-world datasets. Our source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/wendydidi/MISO-PCN</uri> .

SPC: Self-supervised point cloud completion.

3D vehicle shape completion is a key task in urban environment reconstruction, since the available sensors and 3D scanning procedures (such as Mobile Laser Scanning) in an outdoor city scene cannot usually extract the entire car shapes, which can be necessary for specifying their geometric properties for further analysis or realistic visualization. In this paper, we propose a novel multi-view based 3D object point cloud completion technique. In contrast to existing approaches, our method operates on 2D images formed by projecting the point cloud from several virtual camera positions around the object of interest. Both color and geometrical information is considered during the process, generating dense textured point clouds, displaying realistic patterns in the missing regions from the partial inputs. We present both quantitative and qualitative tests on various synthetic and real laser scanned vehicle point clouds, which demonstrate that our method surpasses existing state-of-the-art approaches. By applying it to vehicles from the Shapenet dataset, our approach outperforms recent techniques in terms of Earth Mover’s Distance (EMD) and Chamfer Distance (CD) by 43.8% and 12.17%, respectively.

Point cloud data in the real world is often affected by occlusion and light reflection, leading to incompleteness of the data. Large‐region missing point clouds will cause great deviations in downstream tasks. A dual feature fusion network (DFF‐Net) is proposed to improve the accuracy of the completion of a large missing region of the point cloud. First, a dual feature encoder is designed to extract and fuse the global and local features of the input point cloud. Subsequently, a decoder is used to directly generate a point cloud of missing region that retains local details. In order to make the generated point cloud more detailed, a loss function with multiple terms is employed to emphasise the distribution density and visual quality of the generated point cloud. A large number of experiments show that the authors’ DFF‐Net is better than the previous state‐of‐the‐art methods in the aspect of point cloud completion.

In this paper, we tackle the challenging problem of point cloud completion from the perspective of feature learning. Our key observation is that to recover the underlying structures as well as surface details, given partial input, a fundamental component is a good feature representation that can capture both global structure and local geometric details. We accordingly first propose FSNet, a feature structuring module that can adaptively aggregate point-wise features into a 2D structured feature map by learning multiple latent patterns from local regions. We then integrate FSNet into a coarse-to-fine pipeline for point cloud completion. Specifically, a 2D convolutional neural network is adopted to decode feature maps from FSNet into a coarse and complete point cloud. Next, a point cloud upsampling network is used to generate a dense point cloud from the partial input and the coarse intermediate output. To efficiently exploit local structures and enhance point distribution uniformity, we propose IFNet, a point upsampling module with a self-correction mechanism that can progressively refine details of the generated dense point cloud. We have conducted qualitative and quantitative experiments on ShapeNet, MVP, and KITTI datasets, which demonstrate that our method outperforms state-of-the-art point cloud completion approaches.

Point cloud completion recovers the complete point clouds from partial ones, providing numerous point cloud information for downstream tasks such as 3-D reconstruction and target detection. However, previous methods usually suffer from unstructured prediction of points in local regions and the discrete nature of the point cloud. To resolve these problems, we propose a point cloud completion network called TPDC. Representing the point cloud as a set of unordered features of points with local geometric information, we devise a Triangular Pyramid Extractor (TPE), using the simplest 3-D structure-a triangular pyramid-to convert the point cloud to a sequence of local geometric information. Our insight of revealing local geometric information in a complex environment is to design a Divide-and-Conquer Splitting Module in a Divide-and-Conquer Splitting Decoder (DCSD) to learn point-splitting patterns that can fit local regions the best. This module employs the Divide-and-Conquer approach to parallelly handle tasks related to fitting ground-truth values to base points and predicting the displacement of split points. This approach aims to make the base points align more closely with the ground-truth values while also forecasting the displacement of split points relative to the base points. Furthermore, we propose a more realistic and challenging benchmark, ShapeNetMask, with more random point cloud input, more complex random item occlusion, and more realistic random environmental perturbations. The results show that our method outperforms both widely used benchmarks as well as the new benchmark.

DcTr: Noise-robust point cloud completion by dual-channel transformer with cross-attention

Point cloud data acquired through 3D scanning is frequently subject to fragmentation due to the constraints of the scanner’s field of view and occlusions within the scanned object. The ensuing incompleteness in the data can significantly degrade the accuracy of subsequent computational tasks. Traditional methods for predicting complete point clouds from these fragments often fail to capture the fine-grained local details, leading to inaccurate reconstructions. In this work, we introduce a novel neural network architecture designed for point cloud completion that addresses these limitations.Our network accepts an incomplete point cloud and employs a multi-scale feature extraction module, which integrates an offset attention mechanism alongside a feature aggregation module operating across various scales. This dual approach significantly bolsters the network’s capacity to discern both local and global features inherent in the point cloud data. Furthermore, we incorporate a seed generation module within our missing point cloud generator, harnessing a hierarchical feature pyramid network to forecast the entirety of the point cloud. This innovative strategy allows our network to accurately predict the structure of missing regions.Empirical evaluations conducted on the Shapenet-Part and ModelNet10 datasets substantiate the efficacy of our proposed methodology. Our approach outperforms the state-of-the-art PF-Net algorithm, achieving a remarkable reduction in chamfer distance by 16.15$\%$ and 41.87$\%$ on the respective datasets. Visual inspection of the results underscores the robust generalization capabilities of our algorithm, which is particularly evident in scenarios with limited dataset sizes. It adeptly predicts the contours of the missing regions and synthesizes a more comprehensive point cloud shape.

In this paper we study the task of a single-view image-guided point cloud completion. Existing methods have got promising results by fusing the information of image into point cloud explicitly or implicitly. However, given that the image has global shape information and the partial point cloud has rich local details, We believe that both modalities need to be given equal attention when performing modality fusion. To this end, we propose a novel dual-channel modality fusion network for image-guided point cloud completion(named DMF-Net), in a coarse-to-fine manner. In the first stage, DMF-Net takes a partial point cloud and corresponding image as input to recover a coarse point cloud. In the second stage, the coarse point cloud will be upsampled twice with shape-aware upsampling transformer to get the dense and complete point cloud. Extensive quantitative and qualitative experimental results show that DMF-Net outperforms the state-of-the-art unimodal and multimodal point cloud completion works on ShapeNet-ViPC dataset.

Point cloud is the popular researches in the industry, and the research on partial point cloud is favored by many researchers. In the acquisition process of original 3D point cloud data, there are partial or sparse problems due to occlusion, lighting and other reasons, which will lead to deviation in downstream tasks. We propose a network MDPCN using multiple decoders, which can make the partial point cloud complete. The network uses deep learning to get multiple features of the partial point cloud, and uses multiple identical decoders to decode. Each decoder uses the self-attention module and the Folding-Net to generate smooth point clouds from the features, and finally integrates the point clouds generated by each decoder to obtain dense point cloud. Compared with other mainstream point cloud completion network methods and ablation experiments, it can be proved that this network model can generate more accurate point cloud and effectively complete the 3D reconstruction task.

Completing point clouds using structural constraints for large-scale points absence in 3D building reconstruction

Point cloud completion is an interesting and challenging task in 3D vision, aiming to recover complete shapes from sparse and incomplete point clouds. Existing learning-based methods often require vast computation cost to achieve excellent performance, which limits their practical applications. In this paper, we propose a novel Recurrent Forward Network (RFNet), which is composed of three modules: Recurrent Feature Extraction (RFE), Forward Dense Completion (FDC) and Raw Shape Protection (RSP). The RFE extracts multiple global features from the incomplete point clouds for different recurrent levels, and the FDC generates point clouds in a coarse-to-fine pipeline. The RSP introduces details from the original incomplete models to refine the completion results. Besides, we propose a Sampling Chamfer Distance to better capture the shapes of models and a new Balanced Expansion Constraint to restrict the expansion distances from coarse to fine. According to the experiments on ShapeNet and KITTI, our network can achieve the state-of-the-art with lower memory cost and faster convergence.

Chapter 5 - Data Preprocessing and Feature Extraction

Introduction and aimsIntraoral scanning (IOS) captures real-time surface morphology of teeth and is widely used in clinical dentistry. However, due to the complex intraoral environment, data loss during scanning is common, leading to incomplete three-dimensional (3D) point clouds. This study aimed to develop and evaluate a deep learning–based method to automatically restore missing regions in intraoral 3D point clouds, thereby improving the accuracy and efficiency of digital orthodontic workflows.MethodsA Point Fractal Network architecture was adopted to reconstruct incomplete IOS data. A dataset comprising 314 IOS scans and 4162 individual teeth was used for training and validation. Missing data were simulated by removing random portions of point clouds (5%, 10%, 15%, and 20%). Model performance was assessed using Chamfer distance (CD) to measure the accuracy of point cloud completion across different levels of data loss.ResultsThe proposed method achieves robust performance, maintaining average CD values below 0.01 across most levels of simulated data loss. Visual comparisons confirmed high geometric fidelity between the completed and original point clouds. Furthermore, the model demonstrated efficient processing, completing each point cloud in approximately 0.5 seconds, enabling near real-time restoration during clinical scanning.ConclusionThe deep learning–based model accurately restores missing IOS data, improving the precision and efficiency of digital dental workflows. Its speed and accuracy support real-time clinical applications and reduce reliance on manual corrections.Clinical RelevanceThis method improves clinical efficiency, reduces chairside time, and enhances both patient comfort and treatment acceptance. In addition, it minimises human error and increases the precision of dental restorations. As digital dentistry continues to evolve, this approach holds great potential for improving the accuracy and efficiency of dental treatments, paving the way for broader artificial intelligence integration in clinical practice.