TPDC: Point Cloud Completion by Triangular Pyramid Features and Divide-and-Conquer in Complex Environments.

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.

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

SPC: Self-supervised point cloud completion.

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.

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> .

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

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 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.

Domain Adaptation (DA) has brought significant progress to a wide range of computer vision tasks. However, there are few methods that explores domain adaptation on 3D point cloud data, which plays crucial role in 3D recognition tasks (i.e., autonomous driving and robotics). In a nutshell, the unique challenges of point cloud data consist of abundant spatial global and local geometric information, complicated data distribution, and more ambiguous margins. In this paper, we introduce a novel Bi-Adversarial Discrepancy Minimization network (BADM), which aligns the cross-domain point cloud data by adopting two adversarial learning units with respect to global representations. Based on PointNet, we also utilize local alignment to reduce the discrepancy of local structures. In addition, our BADM method helps define more accurate margin and decision bounds. For this 3D point cloud DA scenario, we use PointDA-10 as the benchmark, which is extracted from ModelNet, ShapeNet, and ScanNet for cross-domain 3D object classification (10 mutual classes). Extensive experiments demonstrate that our approach achieves state-of-the-art performance, outperforming other 3D DA approaches.

The point cloud is disordered and unstructured, and it is difficult to extract detailed features. The detailed part of the target shape is difficult to complete in the point cloud completion task. It proposes a point cloud completion network (BCA-Net) focusing on detail reconstruction, which can reduce noise and refine shapes. Specifically, it utilizes residual deformation architecture to avoid error points. The break and recombine refinement method is used to recover complete point cloud details. In addition, it proposes a bilateral confidence aggregation unit based on recurrent path aggregation to refine the coarse point cloud shape using multiple gating. Our experiments on the ShapeNet and Complete3D datasets demonstrate that our network performs better than other point cloud completion networks.

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 completion is essential for restoring 3D geometric data lost due to occlusions or sensor limitations. Existing methods often rely on k-nearest neighbor(KNN)-based local feature extraction, which focuses on neighborhoods around central points while neglecting critical global structural information. Additionally, Transformer-based approaches, while effective at modeling global context, typically use central point feature sequences to reduce computational complexity. This windowed attention strategy compromises the preservation of global context, leading to incomplete modeling of the point cloud's overall structure. To address these challenges, we propose SDA-Net, a dual-attention point cloud completion network utilizing multiple serialization strategies. These strategies transform unordered point clouds into structured sequences, enabling comprehensive modeling of inter-point relationships. Additionally, the dual-attention mechanism enhances global feature extraction through complementary spatial and channel-wise self-attention, effectively compensating for the loss of global context. Extensive experiments demonstrate that SDA-Net achieves state-of-the-art performance, including an average Chamfer Distance (CD) of 6.48 on the PCN dataset. Furthermore, it excels in real-world applications, accurately reconstructing fine-grained details in LiDAR-scanned point clouds.

By formulating the data generation as a sequence procedure of denoising autoencoding, diffusion models have achieved superior in-painting performance on image data and beyond. Nevertheless, it is not trivial when capitalizing on diffusion models to generate missing 3D points. The difficulty originates from the intrinsic structure where 3D point cloud is a set of unordered and irregular coordinates. That motivates us to delve into the 3D structural information for designing point cloud encoder-decoder and shape latent generator, to precisely formulate the latent distribution of the complete point cloud and partial observation. In this paper, we propose Point cloud completion with Latent Diffusion Models (PointLDM), a new approach that leverages the conditional denoising diffusion probabilistic modeling (DDPM) in the 3D latent space for shape reconstruction. The architecture of PointLDM consists of a transformer-based variational auto-encoder (VAE) to model the complete shape latent, and a diffusion network for shape latent prediction. The encoder of VAE exploits both of global shape latent and local point features in shape distribution learning. With the learnt shape latent, the decoder first decodes the shape latent into coarse points, and then recovers the fine-grained details around each coarse point by deforming a 2D grid. To reconstruct the shape latent from partial observation, the diffusion network treats the partial observation as the conditional input and generates the shape latent via DDPM. Extensive experiments conducted on MVP, Completion3D, and KITTI quantitatively and qualitatively demonstrate the efficacy of PointLDM over the state-of-the-art shape completion approaches.

Accurate reconstruction of shipwreck structures is a critical requirement in maritime accident search and rescue as well as salvage operations. However, due to the complex underwater environment, the point cloud data collected from shipwrecks are often incomplete. To address this issue, this paper proposes a model for shipwreck point cloud completion and reconstruction based on AdaPoinTr and surface constrained upsampling. Our model first extracts complex geometric structure information from incomplete shipwreck point clouds using AdaPoinTr, then efficiently captures both local and global features of the point clouds through an adaptive geometry-aware Transformer, achieving precise shipwreck point cloud completion. In order to further improve the quality of shipwreck point cloud reconstruction, we have also introduced a surface constraint-based upsampling technique, which better retains geometric details during the refinement process. We simulated an underwater shipwreck scenario using the ShapeNet55 dataset and constructed a shipwreck point cloud dataset for training, testing, and validation. Experimental results show that the proposed method performs excellently in the point cloud completion and reconstruction task for shipwreck scenarios.