3D shape segmentation is an essential task in computer graphics and is widely used in many applications. It plays a critical role in understanding the structure and semantics of 3D models. Traditional approaches to 3D shape segmentation primarily rely on geometric features to partition models into meaningful components. However, these methods often struggle when the geometric characteristics of different parts are similar, resulting in ambiguous segmentation outcomes. To address this fundamental limitation, we introduce a novel text-driven multi-modal framework that systematically integrates textual semantics with geometric analysis for enhanced 3D shape segmentation. Our approach leverages a pre-trained language model with prefix tuning to bridge the semantic granularity gap between part-level annotations and face-level segmentation, while a specialized mesh self-attention module captures contextual relationships among neighboring faces. We design an attention-based text-driven integration mechanism that dynamically weights multimodal features, complemented by a Laplace-Adaptive Attention Module (LAAM) that better handles the distributions of geometric features. Through contrastive learning, we align textual and geometric representations in a shared semantic space, enabling effective disambiguation of geometrically similar but semantically distinct parts. We also contribute the Fine-grained HumanBody benchmark for comprehensive evaluation. Extensive experiments on Princeton Segmentation Benchmark, COSEG, ShapeNetCore, and our proposed benchmark demonstrate that our method significantly outperforms existing approaches, achieving superior segmentation accuracy while effectively resolving geometric ambiguities through semantic understanding.

3D shape segmentation is a fundamental problem in computer vision, supporting applications such as shape reconstruction and semantic understanding. A persistent challenge in learning-based methods is the degradation of performance near part boundaries, commonly attributed to class-level imbalance. In this work, we reveal that the primary source of this issue instead arises from a pronounced imbalance between marginal and internal areas within partitions in 3D shapes, which is largely overlooked by existing methods and leads to systematically poor boundary discrimination. To address this problem, we propose a marginal-aware segmentation framework that explicitly emphasizes boundary localization and relational modeling. The framework is realized by two implementations that serve complementary purposes. Specifically, the staged variant emphasizes interpretability by identifying marginal faces and refining them through topology-aware subgraphs and a Graph Attention Network (GAT), while the end-to-end differentiable integration incorporates our marginal-aware modeling principle into a modern 3D segmentation pipeline (SAMPart3D) by leveraging SAM-derived boundary cues to demonstrate performance gains on state-of-the-art systems. Extensive experiments on PSB, COSEG, and HumanBody demonstrate that the staged variant substantially improves boundary recognition, yielding a 12.32% gain in boundary accuracy over state-of-the-art methods, while the end-to-end integration further improves mIoU from 53.7% to 55.3% on PartObjaverse-Tiny with a modest computational overhead. These results demonstrate that explicit marginal-aware relational modeling is an effective and flexible strategy for improving 3D shape segmentation, particularly in challenging boundary regions.

Technical debt refers to suboptimal solutions adopted for short-term goals. Self-admitted technical debt (SATD) is the debt that is explicitly marked through comments or documentation, making it traceable. Multi-classification of SATD helps developers understand different debt types and improve efficiency. This paper proposes a SATD multi-classification method based on Fine-Tuning the GPT-3.5-turbo model for SATD prediction. This study uses a public dataset containing 10 projects with code comments. We classify design debt, requirement debt, and defect debt and evaluate our method’s performance. The experimental results show that compared to the best baseline model, our method achieves average improvements of 11.41%, 1.72% and 3.72% in MacroF, MacroP and MacroR metrics, respectively, in the MTO scenario. In the OTO scenario, improvements are 2.33%, 3.70% and 2.18%, respectively. These results indicate that our method has a strong generalization ability in SATD multi-classification and offers a new approach to managing technical debt.

研究了m台可用时间不同的同类机可中断调度问题,目标是极小化最大完工时间。每台机器都有一个不同的加工速度和可用时间。通过将真实机器转化成虚拟机器的方法给出最优调度目标值的下界。在这些虚拟机器中,可用时间越早的机器在任何时候都有更快的速度。对该问题,给出一个时间复杂度为O(nm+m2)的最优调度算法,并且该算法的中断次数不超过 $${1\over 2}(m^{2}+3m)-2$$ (n代表工件数量)。

3D shape segmentation is a crucial task in the field of multimedia analysis and processing, and recent years have seen a surge in research on this topic. However, many existing methods only consider geometric features of 3D shapes and fail to explore the potential connections between faces, limiting their segmentation performance. In this paper, we propose a novel segmentation approach that mines and enhances the potential consistency of 3D shapes to overcome this limitation. The key idea is to mine the consistency between different partitions of 3D shapes and to use the unique consistency enhancement strategy to continuously optimize the consistency features for the network. Our method also includes a comprehensive set of network structures to mine and enhance consistent features, enabling more effective feature extraction and better utilization of contextual information around each face when processing complex shapes. We evaluate our approach on public benchmarks through extensive experiments and demonstrate its effectiveness in achieving higher accuracy than existing methods.

In the field of 3D point cloud generation, numerous 3D generative models have demonstrated the ability to generate diverse and realistic 3D shapes. However, the majority of these approaches struggle to generate controllable 3D point cloud shapes that meet user-specific requirements, hindering the large-scale application of 3D point cloud generation. To address the challenge of lacking control in 3D point cloud generation, we are the first to propose controlling the generation of point clouds by shape structures that comprise part existences and part adjacency relationships. We manually annotate the adjacency relationships between the segmented parts of point cloud shapes, thereby constructing a StructureGraph representation. Based on this StructureGraph representation, we introduce StrucADT, a novel structure-controllable point cloud generation model, which consists of StructureGraphNet module to extract structure-aware latent features, cCNF Prior module to learn the distribution of the latent features controlled by the part adjacency, and Diffusion Transformer module conditioned on the latent features and part adjacency to generate structure-consistent point cloud shapes. Experimental results demonstrate that our structure-controllable 3D point cloud generation method produces high-quality and diverse point cloud shapes, enabling the generation of controllable point clouds based on user-specified shape structures and achieving state-of-the-art performance in controllable point cloud generation on the ShapeNet dataset.

The task of 3D shape captioning occupies a significant place within the domain of computer graphics and has garnered considerable interest in recent years. Traditional approaches to this challenge frequently depend on the utilization of costly voxel representations or object detection techniques, yet often fail to deliver satisfactory outcomes. To address the above challenges, in this paper, we introduce Diff-3DCap, which employs a sequence of projected views to represent a 3D object and a continuous diffusion model to facilitate the captioning process. More precisely, our approach utilizes the continuous diffusion model to perturb the embedded captions during the forward phase by introducing Gaussian noise and then predicts the reconstructed annotation during the reverse phase. Embedded within the diffusion framework is a commitment to leveraging a visual embedding obtained from a pre-trained visual-language model, which naturally allows the embedding to serve as a guiding signal, eliminating the need for an additional classifier. Extensive results of our experiments indicate that Diff-3DCap can achieve performance comparable to that of the current state-of-the-art methods.

Identifying points of interest (POIs) on the surface of 3D shapes is a significant challenge in geometric processing research. The complex connection between POIs and their geometric descriptors, combined with the small percentage of POIs on the shape, makes detecting POIs on any given 3D shape a highly challenging task. Existing methods directly detect POIs from the entire 3D shape, resulting in low efficiency and accuracy. Therefore, we propose a novel multi-modal POI detection method using a coarse-to-fine approach, with the key idea of reducing data complexity and enabling more efficient and accurate subsequent POI detection by first identifying and processing important regions on the 3D shape. It first obtains important areas on the 3D shape through 2D projected images, then processes points within these regions using attention mechanisms. Extensive experiments demonstrate that our method outperforms existing POI detection techniques.

This paper presents GaussEdit, a framework for adaptive 3D scene editing guided by text and image prompts. GaussEdit leverages 3D Gaussian Splatting as its backbone for scene representation, enabling convenient Region of Interest selection and efficient editing through a three-stage process. The first stage involves initializing the 3D Gaussians to ensure high-quality edits. The second stage employs an Adaptive Global-Local Optimization strategy to balance global scene coherence and detailed local edits and a category-guided regularization technique to alleviate the Janus problem. The final stage enhances the texture of the edited objects using a sophisticated image-to-image synthesis technique, ensuring that the results are visually realistic and align closely with the given prompts. Our experimental results demonstrate that GaussEdit surpasses existing methods in editing accuracy, visual fidelity, and processing speed. By successfully embedding user-specified concepts into 3D scenes, GaussEdit is a powerful tool for detailed and user-driven 3D scene editing, offering significant improvements over traditional methods.

Point cloud completion is a vital task focused on reconstructing complete point clouds and addressing the incompleteness caused by occlusion and limited sensor resolution. Traditional methods relying on fixed local region partitioning, such as $k$k-nearest neighbors, which fail to account for the highly uneven distribution of geometric complexity across different regions of a shape. This limitation leads to inefficient representation and suboptimal reconstruction, especially in areas with fine-grained details or structural discontinuities. This paper proposes a point cloud completion framework called Degree-Flexible Point Graph Completion Network (DFG-PCN). It adaptively assigns node degrees using a detail-aware metric that combines feature variation and curvature, focusing on structurally important regions. We further introduce a geometry-aware graph integration module that uses Manhattan distance for edge aggregation and detail-guided fusion of local and global features to enhance representation. Extensive experiments on multiple benchmark datasets demonstrate that our method consistently outperforms state-of-the-art approaches.

Face enhancement aims to improve low-quality face images to a higher-quality level. However, in real-world nighttime scenes, complex degradation factors often affect these images, making it challenging to preserve important facial details. Existing image enhancement algorithms typically focus on independently conducting image super-resolution and brightness enhancement, assuming a fixed degradation level based on simulated training datasets. Nonetheless, real nighttime scenes involve complex degradation processes, where degradation factors dynamically and variably manifest. Therefore, achieving effective face enhancement in such scenarios is particularly daunting. This work analyzes and unveils the multiple factors of low resolution and low illumination during degradation. Based on this analysis, we propose a Bi-factor Degradation Decoupling network. Our method leverages a decoupling network to generate qualitative and quantitative features corresponding to each factor’s degradation degree in the low-quality environment. These features are then combined with robust facial feature constraints to recover the details of low-quality faces. Extensive experiments demonstrate that our method surpasses state-of-the-art approaches in both enhancement and face super-resolution.

The segmentation of 3D shapes is a critical aspect of shape analysis. However, most existing methods for 3D shape segmentation treat each face of the original mesh model with equal importance. This uniform approach becomes problematic in areas where the faces are smaller but denser, especially around the junctions of different segments. In such regions, greater importance should be assigned compared to the flatter areas. To address this issue, this paper proposes a novel 3D shape segmentation method that incorporates attentive nonuniform sampling into the segmentation pipeline. By leveraging a transformer-based mechanism, our method adaptively identifies the intricate details of 3D shapes, calculating varying degrees of attention to each face. Consequently, the mesh model is downsampled by eliminating faces with lower attention, thereby optimizing the segmentation process. Our approach outperforms most state-of-the-art methods on multiple public datasets, making it a promising avenue for future research.

3D shape segmentation is a fundamental and crucial task in the field of image processing and 3D shape analysis. To segment 3D shapes using data-driven methods, a fully labeled dataset is usually required. However, obtaining such a dataset can be a daunting task, as manual face-level labeling is both time-consuming and labor-intensive. In this paper, we present a semi-supervised framework for 3D shape segmentation that uses a small, fully labeled set of 3D shapes, as well as a weakly labeled set of 3D shapes with sparse scribble labels. Our framework first employs an auxiliary network to generate initial fully labeled segmentation labels for the sparsely labeled dataset, which helps in training the primary network. During training, the self-refine module uses increasingly accurate predictions of the primary network to improve the labels generated by the auxiliary network. Our proposed method achieves better segmentation performance than previous semi-supervised methods, as demonstrated by extensive benchmark tests, while also performing comparably to supervised methods.

Geometric deep learning has sparked a rising interest in computer graphics to perform shape understanding tasks, such as shape classification and semantic segmentation. When the input is a polygonal surface, one has to suffer from the irregular mesh structure. Motivated by the geometric spectral theory, we introduce Laplacian2Mesh, a novel and flexible convolutional neural network (CNN) framework for coping with irregular triangle meshes (vertices may have any valence). By mapping the input mesh surface to the multi-dimensional Laplacian-Beltrami space, Laplacian2Mesh enables one to perform shape analysis tasks directly using the mature CNNs, without the need to deal with the irregular connectivity of the mesh structure. We further define a mesh pooling operation such that the receptive field of the network can be expanded while retaining the original vertex set as well as the connections between them. Besides, we introduce a channel-wise self-attention block to learn the individual importance of feature ingredients. Laplacian2Mesh not only decouples the geometry from the irregular connectivity of the mesh structure but also better captures the global features that are central to shape classification and segmentation. Extensive tests on various datasets demonstrate the effectiveness and efficiency of Laplacian2Mesh, particularly in terms of the capability of being vulnerable to noise to fulfill various learning tasks.

Detecting points of interest is a fundamental problem in 3D shape analysis and can be beneficial to various tasks in multimedia processing. Traditional learning-based detection methods usually rely on each vertex’s geometric features to discriminate points of interest from other vertices. Observing that points of interest are related to not only geometric features on themselves but also the geometric features of surrounding vertices, we propose a novel context-aware 3D points of interest detection algorithm by adopting the spatial attention mechanism in this article. By designing a context attention module, our approach presents a novel deep neural network to simultaneously pay attention to the geometric features of vertices and their local contexts during extracting points of interest. To obtain satisfactory extraction results, our method adaptively assigns different weights to those features in a data-driven way. Extensive experimental results on SHREC 2007, SHREC 2011, and SHREC 2014 datasets show that our algorithm achieves superior performance over existing methods.

Visual retrieval has been a significant technology in the computer vision task. Visual feature descriptors are the key to the visual retrieval. The famous local feature descriptor is called the Scale Invariant Feature Transform (SIFT), which can keep invariant mapping for the scale, rotate and simulate images. To utilize effectively the SIFT feature descriptor for visual matching on different hardware platforms, this paper proposes an accelerated SIFT algorithm based on the SIFT feature computing principle of the general multi-core platform. First, our multi-core task allocation method introduces the WFM theory into task assignment for each core to improve the core computing resource utilization for high-efficient parallel computing. Then, to improve the efficiency of picture matching, we introduce global geometric constraints condition to optimal picture matching for the multi-core parallelization approach. Experimental results show that the proposed approach can save on average 87.31% on the Intel X86 platform, compared to the single-core time. Also, our approach can save on average 33.79% on the Raspberry Pi platform, compared to the single-core time.

Detecting points of interest on 3D shapes is a fundamental research problem in geometry processing. Due to the complicated relationship between points of interest and their geometric features, detecting points of interest on any given 3D shape remains challenging. Due to the lack of training data, previous data-driven methods for detecting 3D points of interest mainly focus on utilizing hand-crafted geometric features to predict the probabilities of each point being a POI, which greatly limits detection performance. In this paper, we propose a novel algorithm for detecting 3D points of interest by using projective neural networks. Our method first projects the labeled training 3D shapes into multiple 2D views and then learns the required features from the 2D views in an end-to-end fashion. The points of interest on test 3D shapes are then automatically detected by applying the learned neural network and our improved density peak clustering. Our method relies neither on hand-crafted feature descriptors nor a large quantity of expensive 3D training data to obtain satisfactory results. Experimental results show significantly superior detection performance of our method over the state-of-the-art methods.

Shape segmentation is a fundamental problem in shape analysis. Previous research shows that prior knowledge helps to improve the segmentation accuracy and quality. However, completely labeling each 3D shape in a large training data set requires a heavy manual workload. In this paper, we propose a novel weakly-supervised algorithm for segmenting 3D shapes using deep learning. Our method jointly propagates information from scribbles to unlabeled faces and learns deep neural network parameters. Therefore, it does not rely on completely labeled training shapes and only needs a really simple and convenient scribble-based partially labeling process, instead of the extremely time-consuming and tedious fully labeling processes. Various experimental results demonstrate the proposed method's superior segmentation performance over the previous unsupervised approaches and comparable segmentation performance to the state-of-the-art fully supervised methods.

Considering the fact that points of interest on 3D shapes can be discriminated from a geometric perspective, it is reasonable to map the geometric signature of a point $p$p to a probability value encoding to what degree $p$p is a point of interest, especially for a specific class of 3D shapes. Based on the observation, we propose a three-phase algorithm for learning and predicting points of interest on 3D shapes by using multiple feature descriptors. Our algorithm requires two separate deep neural networks (stacked auto-encoders) to accomplish the task. During the first phase, we predict the membership of the given 3D shape according to a set of geometric descriptors using a deep neural network. After that, we train the other deep neural network to predict a probability distribution defined on the surface representing the possibility of a point being a point of interest. Finally, we use a manifold clustering technique to extract a set of points of interest as the output. Experimental results show superior detection performance of the proposed method over the previous state-of-the-art approaches.

Despite rapid advances in 3D printing, fabricating large, durable and robust artifacts is impractical with current technology. We focus on a particularly challenging environment-scale artifact: rock climbing routes. We propose a prototype fabrication method to replicate part of an outdoor climbing route and enable the same sensorimotor experience in an indoor gym. We start with 3D reconstruction of the rock wall using multi-view stereo and use reference videos of a climber in action to identify localized rock features that are necessary for ascent. We create 3D models akin to traditional indoor climbing holds, fabricated using rapid prototyping, molding and casting techniques. This results in robust holds accurately replicating the features and configuration of the original rock route. Validation was performed on two rock climbing sites in New Hampshire and Utah. We verified our results by comparing climbers' moves on the indoor replicas and original outdoor routes.

Shape description and feature detection are fundamental problems in computer graphics and geometric modeling. Among many existing techniques, those based on geodesic distance have proven effective in providing intrinsic and discriminative shape descriptors. In this article we introduce a new intrinsic function for a three-dimensional (3D) shape and use it for shape description and geometric feature detection. Specifically, we introduce the intrinsic girth function (IGF) defined on a 2D closed surface. For a point p on the surface, the value of the IGF at p is the length of the shortest nonzero geodesic path starting and ending at p . The IGF is invariant under isometry, insensitive to mesh tessellations, and robust to surface noise. We propose a fast method for computing the IGF and discuss its applications to shape retrieval and detecting tips, tubes, and plates that are constituent parts of 3D objects.

In general, the first step for supervised learning from crowdsourced data is integration. To obtain training data as traditional machine learning, the ground truth for each example in the crowdsourcing dataset must be integrated with consensus algorithms. However, some information and correlations among labels in the crowdsourcing dataset have discarded after integration. In order to study whether the information and correlations are useful for learning, we proposed three types of neural networks. Experimental results show that i) all the three types of neural networks have abilities to predict labels for future unseen examples, ii) when labelers have lower qualities, the information and correlations in crowdsourcing datasets, which are discarded by integration, does improve the performance of neural networks significantly, iii) when labelers have higher label qualities, the information and correlations have little impact on improving accuracy of neural networks.

With various online crowdsourcing platforms, it is easy to collect multiple labels for the same examples from the crowd. Consensus integration algorithms can infer the estimated ground truths from the multiple label sets of these crowdsourcing datasets. However, it couldn't be avoided that these integrated (estimated) labels still contain noises. In order to further improve the performance of a model learned from data with these integrated labels, we propose an active learning framework to further improve the data quality, such that to improve the model quality, through acquiring limited true labels from experts (the oracle). We further investigate two active learning strategies in terms of two uncertainty measures (i.e., CLUE and MUE) within the active learning framework. From our experimental results on eight simulation crowdsourcing datasets and four real-world crowdsourcing datasets with three popular consensus integration algorithms, we draw several conclusions as follows. (i) Our active learning framework with the input from the oracle significantly improves the generalization ability of the model learned from crowdsourcing data. (ii) Our two active learning strategies outperform a random active learning strategy.