WIR3D: Visually-Informed and Geometry-Aware 3D Shape Abstraction

• URL: http://arxiv.org/abs/2505.04813v1
• Date: Wed, 07 May 2025 21:28:05 GMT
• Title: WIR3D: Visually-Informed and Geometry-Aware 3D Shape Abstraction
• Authors: Richard Liu, Daniel Fu, Noah Tan, Itai Lang, Rana Hanocka,
• Abstract summary: WIR3D is a technique for abstracting 3D shapes through a sparse set of visually meaningful curves in 3D.<n>We optimize the parameters of Bezier curves such that they faithfully represent both the geometry and salient visual features.<n>We successfully apply our method for shape abstraction over a broad dataset of shapes.
• Score: 13.645442589551354
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present WIR3D, a technique for abstracting 3D shapes through a sparse set of visually meaningful curves in 3D. We optimize the parameters of Bezier curves such that they faithfully represent both the geometry and salient visual features (e.g. texture) of the shape from arbitrary viewpoints. We leverage the intermediate activations of a pre-trained foundation model (CLIP) to guide our optimization process. We divide our optimization into two phases: one for capturing the coarse geometry of the shape, and the other for representing fine-grained features. Our second phase supervision is spatially guided by a novel localized keypoint loss. This spatial guidance enables user control over abstracted features. We ensure fidelity to the original surface through a neural SDF loss, which allows the curves to be used as intuitive deformation handles. We successfully apply our method for shape abstraction over a broad dataset of shapes with varying complexity, geometric structure, and texture, and demonstrate downstream applications for feature control and shape deformation.

Related papers

• NeuVAS: Neural Implicit Surfaces for Variational Shape Modeling [54.06448198674519]
  NeuVAS is a variational approach to shape modeling using neural implicit surfaces constrained under sparse input shape control.<n>We introduce a smoothness term based on a functional of surface curvatures to minimize shape variation of the zero-level set surface of a neural SDF.
  arXiv  Detail & Related papers  (2025-06-16T02:39:45Z)
• DECOLLAGE: 3D Detailization by Controllable, Localized, and Learned Geometry Enhancement [38.719572669042925]
  We present a 3D modeling method which enables end-users to refine or detailize 3D shapes using machine learning. We show that our ability to localize details enables novel interactive creative and applications.
  arXiv  Detail & Related papers  (2024-09-10T00:51:49Z)
• 3D Neural Edge Reconstruction [61.10201396044153]
  We introduce EMAP, a new method for learning 3D edge representations with a focus on both lines and curves. Our method implicitly encodes 3D edge distance and direction in Unsigned Distance Functions (UDF) from multi-view edge maps. On top of this neural representation, we propose an edge extraction algorithm that robustly abstracts 3D edges from the inferred edge points and their directions.
  arXiv  Detail & Related papers  (2024-05-29T17:23:51Z)
• 3D Geometry-aware Deformable Gaussian Splatting for Dynamic View Synthesis [49.352765055181436]
  We propose a 3D geometry-aware deformable Gaussian Splatting method for dynamic view synthesis. Our solution achieves 3D geometry-aware deformation modeling, which enables improved dynamic view synthesis and 3D dynamic reconstruction.
  arXiv  Detail & Related papers  (2024-04-09T12:47:30Z)
• Robust 3D Tracking with Quality-Aware Shape Completion [67.9748164949519]
  We propose a synthetic target representation composed of dense and complete point clouds depicting the target shape precisely by shape completion for robust 3D tracking. Specifically, we design a voxelized 3D tracking framework with shape completion, in which we propose a quality-aware shape completion mechanism to alleviate the adverse effect of noisy historical predictions.
  arXiv  Detail & Related papers  (2023-12-17T04:50:24Z)
• Parameterization-driven Neural Surface Reconstruction for Object-oriented Editing in Neural Rendering [35.69582529609475]
  This paper introduces a novel neural algorithm for parameterizing neural implicit surfaces to simple parametric domains like spheres and polycubes. It computes bi-directional deformation between the object and the domain using a forward mapping from the object's zero level set and an inverse deformation for backward mapping. We demonstrate the method's effectiveness on images of human heads and man-made objects.
  arXiv  Detail & Related papers  (2023-10-09T08:42:40Z)
• DragD3D: Realistic Mesh Editing with Rigidity Control Driven by 2D Diffusion Priors [10.355568895429588]
  Direct mesh editing and deformation are key components in the geometric modeling and animation pipeline. Regularizers are not aware of the global context and semantics of the object. We show that our deformations can be controlled to yield realistic shape deformations aware of the global context.
  arXiv  Detail & Related papers  (2023-10-06T19:55:40Z)
• SepicNet: Sharp Edges Recovery by Parametric Inference of Curves in 3D Shapes [16.355677959323426]
  We introduce SepicNet, a novel deep network for the detection and parametrization of sharp edges in 3D shapes as primitive curves. We develop an adaptive point cloud sampling technique that captures the sharp features better than uniform sampling.
  arXiv  Detail & Related papers  (2023-04-13T13:37:21Z)
• MvDeCor: Multi-view Dense Correspondence Learning for Fine-grained 3D Segmentation [91.6658845016214]
  We propose to utilize self-supervised techniques in the 2D domain for fine-grained 3D shape segmentation tasks. We render a 3D shape from multiple views, and set up a dense correspondence learning task within the contrastive learning framework. As a result, the learned 2D representations are view-invariant and geometrically consistent.
  arXiv  Detail & Related papers  (2022-08-18T00:48:15Z)
• 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)
• SeqXY2SeqZ: Structure Learning for 3D Shapes by Sequentially Predicting 1D Occupancy Segments From 2D Coordinates [61.04823927283092]
  We propose to represent 3D shapes using 2D functions, where the output of the function at each 2D location is a sequence of line segments inside the shape. We implement this approach using a Seq2Seq model with attention, called SeqXY2SeqZ, which learns the mapping from a sequence of 2D coordinates along two arbitrary axes to a sequence of 1D locations along the third axis. Our experiments show that SeqXY2SeqZ outperforms the state-ofthe-art methods under widely used benchmarks.
  arXiv  Detail & Related papers  (2020-03-12T00:24:36Z)

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.