Point cloud completion aims to reconstruct complete shapes from partial data. Single-modal methods, constrained by limited prior information, see a substantial boost in completion accuracy with additional guidance provided by image modality. However, most existing methods still adopt encoder-decoder architectures, which tends to cause information loss during compression and decompression operations. To address this, we propose the Patch-wise Deformation Network for cross-modal completion. Rather than encode shapes into high-dimensional representations and subsequently decode them, this strategy directly leverages deformations on point cloud in Euclidean space to retain original information adequately. Specifically, we first split input point cloud into similar patches. Then Patch Deformation module decomposes shape attributes from cross-modal inputs into structural and semantic components as guidance for deformations, enabling high-fidelity completion results. Extensive experiments demonstrate our method’s superiority in point cloud completion task.

Cross-modal retrieval with 3D models has gained significant attention with the rapid growth of 3D assets. The core challenge lies in learning modality-invariant and discriminative features in a common space. Existing methods often rely on shared class centers in Euclidean space, overlooking directional relationships between samples and non-corresponding centers, while remaining sensitive to modality-specific scales, hindering the learning of discriminative cross-modal centers, especially for dispersed modalities like real-world images. To address these limitations, we propose the Discriminative Cosine Center Learning (DCCL) framework for 3D cross-modal retrieval. DCCL integrates the Adaptive Cosine Center Learning (ACCL) mechanism, optimizing cosine similarity on a shared hypersphere with adaptive penalties for challenging samples. Additionally, the Cross-Modal Affinity Learning (CMAL) mechanism reduces cross-modal discrepancies by pairwise matching data from different modalities. Extensive experiments on five benchmarks demonstrate that DCCL significantly outperforms baseline methods in both synthetic and real-world scenarios.

Point clouds captured by scanning devices are often sparse and incomplete. Most existing methods for point cloud completion use 3D coordinates only to infer geometric shapes, making it difficult to reconstruct accurate structures and details. We propose a novel cross-modal approach, called Multi-Scale Cross-modal Network for Point Cloud Completion (MSC-Net), which leverages the information of image modality to guide the geometric inference of missing parts. In order to obtain more abundant geometric information, we extract multi-scale features of partial point cloud. Then we design a feature fusion module, which employs multi-layer cross-attention to achieve the interaction between the image features and point cloud features at different scales. To improve the ability of local feature perception, we further devise an enhanced cross-attention block. In the decoding stage, we adopt a coarse-to-fine strategy, where the geometry-aware upsampling layer is utilized to refine the point cloud step by step. Experiments and ablation studies have demonstrated the effectiveness of our network, proving that our approach outperforms existing approaches.

Point clouds serve as the foundational representation of 3D objects, playing a pivotal role in both computer vision and computer graphics. Recently, the acquisition of point clouds has been effortless because of the development of hardware devices. However, the collected point clouds may be incomplete due to environmental conditions, such as occlusion. Therefore, completing partial point clouds becomes an essential task. The majority of current methods address point cloud completion via the utilization of shape priors. While these methods have demonstrated commendable performance, they often encounter challenges in preserving the global structural and geometric details of the 3D shape. In contrast to those mentioned earlier, we propose a novel cross-modal coarse-to-fine network (CMNet) for point cloud completion. Our method utilizes additional image information to provide global information, thus avoiding the loss of structure. To ensure that the generated results contain sufficient geometric details, we propose a coarse-to-fine learning approach based on multiple patches. Specifically, we encode the image and use multiple generators to generate multiple coarse patches, which are combined into a complete shape. Subsequently, based on the coarse patches generated in advance, we generate fine patches by combining partial point cloud information. Experimental results show that our method achieves state-of-the-art performance on point cloud completion.

Point cloud completion aims at completing shapes from their partial. Most existing methods utilized shape’s priors information for point cloud completion, such as inputting the partial and getting the complete one through an encoder-decoder deep learning structure. However, it is very often to easily cause the loss of information in the generation process because of the invisibility of missing areas. Unlike most existing methods directly inferring the missing points using shape priors, we address it as a cross-modality task. We propose a new Cross-modal Dual Phases Network (CDPNet) for shape completion. Our key idea is that the global information of the shape is obtained from the extra single-view image, and the partial point clouds provide the geometric information. After that, the multi-modal features jointly guide the specific structural information. To learn the geometric details of the shape, we chose to use patches to preserve the local geometric feature. In this way, we can generate shapes with enough geometric details. Experimental results show that our method achieves state-of-the-art performance on point cloud completion.

In this paper, we introduce a novel structure-aware method to generate diverse, and realistic 3D shapes via semantic parts. Most previous works neglect the structural and context information between shape parts and only consider the geometric information. This sometimes leads to the wrong combination of parts during the generation process and brought down the generation quality. To address this issue, we learn a structure-aware latent representation for 3D shapes by training a variational autoencoder(VAE). Specially, we use a graph to express semantic parts and their structural relationship of the 3D shape. Based on that graph representation, we design a generative network based on graph transformer architecture, called Graph Transformer VAE networks(GT-Net), to encode and decode the graph-represented 3D shape. Our experimental results demonstrate that our method achieves better performance than previous methods among various shape families, especially in terms of capturing shape details information.

Point cloud completion has got increasingly attention recently. Its task is to predict a complete point cloud from a partial one, which plays a vital role in three-dimension technology. In order to better obtain the multi-level local information of the point cloud and better combine the local information with the global information for analysis, we proposed a novel Generative Adversarial Network(GAN) for point cloud completion, which called Multi-Resolution Relation-Aware GAN(MRRA-GAN). We designed a Multi-Resolution Key Points Generator(MKPG) which uses multi-resolution point cloud as input to construct key points, a Point Cloud Tree Generator(PTG) to construct Point Cloud Tree(PCT) and a penalty item called Uniformity Penalty(UP) to increase the uniformity of the output point cloud. Experiments, ablation study and robustness test demonstrate the effectiveness of our network, even chanllenging point cloud with different missing degree.

Point cloud completion has become a popular research area in 3D computer vision. It aims to recover the complete point cloud from its partial observation. However, previous methods either directly predict the whole shape, change the original distribution of points, or have limited performance in reconstructing tiny and detailed object components. In this paper, we propose a novel Patch-based Dual-Path Network (PDP-Net) for point cloud completion, which leverages the advantages of different encoder architectures, with one path providing estimation for the global structure of the missing part, and the other path filling in the details by generating several point cloud patches. We also propose an identifier to retain the original points in the partial point cloud possibly. Comprehensive experiments and robustness tests demonstrate the effectiveness of our method even against different missing scales of the point cloud. Code available at: https://github.com/QifHE/PDP-Net-Public.