Textured Geometry Evaluation: Perceptual 3D Textured Shape Metric via 3D Latent-Geometry Network
- URL: http://arxiv.org/abs/2512.01380v1
- Date: Mon, 01 Dec 2025 07:53:03 GMT
- Title: Textured Geometry Evaluation: Perceptual 3D Textured Shape Metric via 3D Latent-Geometry Network
- Authors: Tianyu Luan, Xuelu Feng, Zixin Zhu, Phani Nuney, Sheng Liu, Xuan Gong, David Doermann, Chunming Qiao, Junsong Yuan,
- Abstract summary: Existing metrics, such as Chamfer Distance, often fail to align with how humans evaluate the fidelity of 3D shapes.<n>Recent learning-based metrics attempt to improve this by relying on rendered images and 2D image quality metrics.<n>We propose a new fidelity evaluation method that is based directly on 3D meshes with texture, without relying on rendering.
- Score: 47.25343775786203
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Textured high-fidelity 3D models are crucial for games, AR/VR, and film, but human-aligned evaluation methods still fall behind despite recent advances in 3D reconstruction and generation. Existing metrics, such as Chamfer Distance, often fail to align with how humans evaluate the fidelity of 3D shapes. Recent learning-based metrics attempt to improve this by relying on rendered images and 2D image quality metrics. However, these approaches face limitations due to incomplete structural coverage and sensitivity to viewpoint choices. Moreover, most methods are trained on synthetic distortions, which differ significantly from real-world distortions, resulting in a domain gap. To address these challenges, we propose a new fidelity evaluation method that is based directly on 3D meshes with texture, without relying on rendering. Our method, named Textured Geometry Evaluation TGE, jointly uses the geometry and color information to calculate the fidelity of the input textured mesh with comparison to a reference colored shape. To train and evaluate our metric, we design a human-annotated dataset with real-world distortions. Experiments show that TGE outperforms rendering-based and geometry-only methods on real-world distortion dataset.
Related papers
- TriaGS: Differentiable Triangulation-Guided Geometric Consistency for 3D Gaussian Splatting [2.441486089588484]
3D Gaussian Splatting is crucial for real-time novel view synthesis due to its efficiency and ability to render images.<n>This paper introduces a novel method that improves reconstruction by enforcing global geometry consistency through constrained multi-view triangulation.<n>We demonstrate the effectiveness of our method across multiple photorealistic datasets, achieving state-of-the-art results.
arXiv Detail & Related papers (2025-12-06T03:45:39Z) - Seeing 3D Through 2D Lenses: 3D Few-Shot Class-Incremental Learning via Cross-Modal Geometric Rectification [59.17489431187807]
We propose a framework that enhances 3D geometric fidelity by leveraging CLIP's hierarchical spatial semantics.<n>Our method significantly improves 3D few-shot class-incremental learning, achieving superior geometric coherence and robustness to texture bias.
arXiv Detail & Related papers (2025-09-18T13:45:08Z) - Dual Enhancement on 3D Vision-Language Perception for Monocular 3D Visual Grounding [46.331376542148696]
Monocular 3D visual grounding is a novel task that aims to locate 3D objects in RGB images using text descriptions with explicit geometry information.<n>We propose to enhance the 3D perception of model on text embeddings and geometry features with two simple and effective methods.
arXiv Detail & Related papers (2025-08-26T16:13:18Z) - Shape from Semantics: 3D Shape Generation from Multi-View Semantics [30.969299308083723]
Existing 3D reconstruction methods utilize guidances such as 2D images, 3D point clouds, shape contours and single semantics to recover the 3D surface.<n>We propose a novel 3D modeling task called Shape from Semantics'', which aims to create 3D models whose geometry and appearance are consistent with the given text semantics when viewed from different views.
arXiv Detail & Related papers (2025-02-01T07:51:59Z) - PaintHuman: Towards High-fidelity Text-to-3D Human Texturing via
Denoised Score Distillation [89.09455618184239]
Recent advances in text-to-3D human generation have been groundbreaking.
We propose a model called PaintHuman to address the challenges from two aspects.
We use the depth map as a guidance to ensure realistic semantically aligned textures.
arXiv Detail & Related papers (2023-10-14T00:37:16Z) - 3D Surface Reconstruction in the Wild by Deforming Shape Priors from
Synthetic Data [24.97027425606138]
Reconstructing the underlying 3D surface of an object from a single image is a challenging problem.
We present a new method for joint category-specific 3D reconstruction and object pose estimation from a single image.
Our approach achieves state-of-the-art reconstruction performance across several real-world datasets.
arXiv Detail & Related papers (2023-02-24T20:37:27Z) - Beyond 3DMM: Learning to Capture High-fidelity 3D Face Shape [77.95154911528365]
3D Morphable Model (3DMM) fitting has widely benefited face analysis due to its strong 3D priori.
Previous reconstructed 3D faces suffer from degraded visual verisimilitude due to the loss of fine-grained geometry.
This paper proposes a complete solution to capture the personalized shape so that the reconstructed shape looks identical to the corresponding person.
arXiv Detail & Related papers (2022-04-09T03:46:18Z) - Texturify: Generating Textures on 3D Shape Surfaces [34.726179801982646]
We propose Texturify to learn a 3D shape that predicts texture on the 3D input.
Our method does not require any 3D color supervision to learn 3D objects.
arXiv Detail & Related papers (2022-04-05T18:00:04Z) - Hybrid Approach for 3D Head Reconstruction: Using Neural Networks and
Visual Geometry [3.970492757288025]
We present a novel method for reconstructing 3D heads from a single or multiple image(s) using a hybrid approach based on deep learning and geometric techniques.
We propose an encoder-decoder network based on the U-net architecture and trained on synthetic data only.
arXiv Detail & Related papers (2021-04-28T11:31:35Z) - Hard Example Generation by Texture Synthesis for Cross-domain Shape
Similarity Learning [97.56893524594703]
Image-based 3D shape retrieval (IBSR) aims to find the corresponding 3D shape of a given 2D image from a large 3D shape database.
metric learning with some adaptation techniques seems to be a natural solution to shape similarity learning.
We develop a geometry-focused multi-view metric learning framework empowered by texture synthesis.
arXiv Detail & Related papers (2020-10-23T08:52:00Z) - Pix2Surf: Learning Parametric 3D Surface Models of Objects from Images [64.53227129573293]
We investigate the problem of learning to generate 3D parametric surface representations for novel object instances, as seen from one or more views.
We design neural networks capable of generating high-quality parametric 3D surfaces which are consistent between views.
Our method is supervised and trained on a public dataset of shapes from common object categories.
arXiv Detail & Related papers (2020-08-18T06:33:40Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.