Research map / Point clouds / Shape and surface completion
Shape as Line Segments: Accurate and Flexible Implicit Surface Representation
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Proposes a line-segment-based implicit surface representation that encodes shapes via segments instead of closest-distance fields, aiming for accurate and flexible surface extraction.
Problem
Distance field-based implicit representations (signed/unsigned distance fields) rely on closest point distances, causing inaccurate interpolation along cube edges during surface extraction and ill-defined gradients at certain locations that distort reconstructions.
Approach
Represents the surface using line segments rather than closest-distance values, avoiding the interpolation inaccuracies and ill-defined gradients of distance fields.
Experimental setup
Baselines: signed distance fields unsigned distance fields
Builds on: signed distance fields unsigned distance fields
Closest papers by meaning
- LISR: Learning Linear 3D Implicit Surface Representation Using Compactly Supported Radial Basis Functions (AAAI 2024 · similarity 0.86)
- 3D Neural Edge Reconstruction (CVPR 2024 · similarity 0.86)
- Unsigned Orthogonal Distance Fields: An Accurate Neural Implicit Representation for Diverse 3D Shapes (CVPR 2024 · similarity 0.86)
- MIND: Material Interface Generation from UDFs for Non-Manifold Surface Reconstruction (NeurIPS 2025 · similarity 0.86)
- DUDF: Differentiable Unsigned Distance Fields with Hyperbolic Scaling (CVPR 2024 · similarity 0.86)
- SpelsNet: Surface Primitive Elements Segmentation by B-Rep Graph Structure Supervision (NeurIPS 2024 · similarity 0.85)
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