Geometric estimation is required for scene understanding and analysis of panoramic 360u00B0 images. Current methods usually predict a single feature, such as depth or surface normal. These methods can lack robustness, especially when dealing with intricate textures or complex object surfaces. We introduce a novel multi-task learning (MTL) network that simultaneously estimates depth and surface normals from 360u00B0 images. Our first innovation is our MTL architecture, which enhances predictions for both tasks by integrating geometric information from depth and surface normal estimation, enabling a deeper understanding of 3D scene structure. Another innovation is our fusion module, which bridges the two tasks, allowing the network to learn shared representations that improve accuracy and robustness. Experimental results demonstrate that our MTL architecture significantly outperforms state-of-the-art methods in both depth and surface normal estimation, showing superior performance for complex and diverse scenes. Our model's effectiveness and generalizability, particularly in handling intricate surface textures, establish it as a new benchmark for 360u00B0 image geometric estimation. The code and model are available at
Surface reconstruction from raw point clouds has been a pivotal focus in computer graphics for numerous years, primarily due to its integral role in modeling and rendering applications. A key approach to tackling this challenge involves establishing local geometries to align with the local curve. However, conventional methodologies typically resort to constructing either a local plane or a polynomial curve. While a local plane may result in the loss of sharp features, and boundary errors on open surfaces, utilizing a polynomial curve can pose difficulties when using neural networks due to issues with local coordinate consistency. In response to these limitations, we introduce a groundbreaking concept known as anglehedral surfaces to represent local geometry. This innovative idea offers enhanced flexibility in portraying sharp features and surface boundaries on open surfaces without necessitating a local coordinate system, which is crucial for neural network integration. Specifically, we leverage normals to construct anglehedral surfaces, encompassing dihedral and trihedral surfaces controlled by 2 and 3 normals, respectively. As a result, our approach adeptly accommodates sharp features on turning surfaces and accurately captures boundaries on open surfaces. We demonstrate superior performance on three widely-used datasets: ShapeNetCore, ABC, and ScanNet. Code is available at http://gitee.com/HuiTian1/3fold.
Point clouds are now a popular data representations in 3D vision research. However, due to the limited performance of sensors, the raw data usually suffer from sparsity, sensing noise, and incompleteness. This poses great challenges to downstream point cloud processing tasks. In recent years, deep-learning-based geometric point cloud enhancement methods are gaining tremendous research attention; they use deep neural networks with the aim of achieving dense, clean, and complete point clouds from low-quality raw point clouds. This paper presents a comprehensive survey of deep-learning-based geometric point cloud enhancement methods. It covers three main kinds of geometric point cloud enhancement: (i) denoising to achieve clean data, (ii) completion to recover unseen data, and (iii) upsampling to obtain dense data. Our survey presents a new taxonomy for recent state-of-the-art methods and systematic experimental results on standard benchmarks. In addition, we share our observations, and suggest future research directions for geometric point cloud enhancement with deep learning.
Parametric garment pattern models are widely used to generate 2D and 3D garment representations in reconstruction and simulation tasks. However, existing models are often difficult to adapt for specific purposes due to limited variations in base pattern shapes and the rigid, hardcoded handling of body and garment measurements. To address these challenges, we propose GarmentoPIA, a system that semi-automatically generates parametric garment pattern models from garment drafting literature using an LLM-based intelligent agent module. With GarmentoPIA, users can select garment drafting references and generate various pattern models that incorporate tailoring parameters specified in the source material. To improve adaptability across references, the system employs a prompt self-refinement mechanism that iteratively updates its instructions during model generation. This generation process is preceded by a manual preprocessing phase that first normalizes raw drafting instructions into a structured format. We furthermore introduce a garment domain-specific language (DSL), composed of explicit function calls corresponding to drafting components, to produce executable models that, once generated, run independently of any LLM—enhancing usability and accessibility. We validate the effectiveness of our approach through quantitative and qualitative evaluations.
