2000 character limit reached
Unsigned Orthogonal Distance Fields: An Accurate Neural Implicit Representation for Diverse 3D Shapes (2403.01414v2)
Published 3 Mar 2024 in cs.CV
Abstract: Neural implicit representation of geometric shapes has witnessed considerable advancements in recent years. However, common distance field based implicit representations, specifically signed distance field (SDF) for watertight shapes or unsigned distance field (UDF) for arbitrary shapes, routinely suffer from degradation of reconstruction accuracy when converting to explicit surface points and meshes. In this paper, we introduce a novel neural implicit representation based on unsigned orthogonal distance fields (UODFs). In UODFs, the minimal unsigned distance from any spatial point to the shape surface is defined solely in one orthogonal direction, contrasting with the multi-directional determination made by SDF and UDF. Consequently, every point in the 3D UODFs can directly access its closest surface points along three orthogonal directions. This distinctive feature leverages the accurate reconstruction of surface points without interpolation errors. We verify the effectiveness of UODFs through a range of reconstruction examples, extending from simple watertight or non-watertight shapes to complex shapes that include hollows, internal or assembling structures.
- Sal: Sign agnostic learning of shapes from raw data. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
- Representing 3d shapes with probabilistic directed distance fields. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 19321–19332, 2022.
- Towards better gradient consistency for neural signed distance functions via level set alignment. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
- The ball-pivoting algorithm for surface reconstruction. IEEE transactions on visualization and computer graphics (TVCG), 5(4):349–359, 1999.
- Multi-garment net: Learning to dress 3d people from images. In IEEE/CVF International Conference on Computer Vision (ICCV), 2019.
- Deep local shapes: Learning local sdf priors for detailed 3d reconstruction. In European Conference on Computer Vision (ECCV), 2020.
- Shapenet: An information-rich 3d model repository. arXiv preprint arXiv:1512.03012, 2015.
- 3psdf: Three-pole signed distance function for learning surfaces with arbitrary topologies. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
- Neural dual contouring. ACM Transactions on Graphics (TOG), 41(4):1–13, 2022.
- Neural unsigned distance fields for implicit function learning. In Neural Information Processing Systems (NeurIPS), 2020.
- A volumetric method for building complex models from range images. In Computer graphics and interactive techniques, 1996.
- On the effectiveness of weight-encoded neural implicit 3d shapes. arXiv preprint arXiv:2009.09808, 2020.
- Implicit geometric regularization for learning shapes. In Proceedings of Machine Learning and Systems, pages 3569–3579. 2020.
- Meshudf: Fast and differentiable meshing of unsigned distance field networks. In European Conference on Computer Vision (ECCV), 2022.
- Pct: Point cloud transformer. Computational Visual Media, 7(2):187–199, Apr. 2021.
- J. C. Hart. Sphere tracing: A geometric method for the antialiased ray tracing of implicit surfaces. Visual Computer, 12(10):527–545, 1996.
- Neuralodf: Learning omnidirectional distance fields for 3d shape representation. 06 2022.
- Screened poisson surface reconstruction. ACM Transactions on Graphics (TOG), 32(3):1–13, Jul. 2013.
- Feature sensitive surface extraction from volume data. In SIGGRAPH, 2001.
- Neudf: Leaning neural unsigned distance fields with volume rendering. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
- Neuraludf: Learning unsigned distance fields for multi-view reconstruction of surfaces with arbitrary topologies. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
- Sparseneus: Fast generalizable neural surface reconstruction from sparse views. In European Conference on Computer Vision (ECCV), 2022.
- Marching cubes: A high resolution 3d surface construction algorithm. Computer Graphics, 21(4):163–169, 1987.
- Instant neural graphics primitives with a multiresolution hash encoding. ACM Transactions on Graphics (TOG), 41(4):102:1–102:15, Jul. 2022.
- DeepCurrents: Learning implicit representations of shapes with boundaries. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
- Deepsdf: Learning continuous signed distance functions for shape representation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
- Pointnet: Deep learning on point sets for 3d classification and segmentation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
- Pointnet++: Deep hierarchical feature learning on point sets in a metric space. In Neural Information Processing Systems (NeurIPS), 2017.
- Implicit neural representations with periodic activation functions. In Neural Information Processing Systems (NeurIPS), 2020.
- Neural geometric level of detail: Real-time rendering with implicit 3d shapes. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021.
- Fourier features let networks learn high frequency functions in low dimensional domains. In Neural Information Processing Systems (NeurIPS), 2020.
- Octfield: Hierarchical implicit functions for 3d modeling. In Neural Information Processing Systems (NeurIPS), 2021.
- Neural density-distance fields. arXiv preprint arXiv:2207.14455, 2022.
- Deep implicit surface point prediction networks. In IEEE/CVF International Conference on Computer Vision (ICCV), pages 12633–12642, 2021.
- Hsdf: Hybrid sign and distance field for modeling surfaces with arbitrary topologies. In Neural Information Processing Systems (NeurIPS), 2022.
- Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction. In Neural Information Processing Systems (NeurIPS), 2021.
- HF-NeuS: Improved Surface Reconstruction Using High-Frequency Details. In Neural Information Processing Systems (NeurIPS), 2022.
- Dynamic graph cnn for learning on point clouds. ACM Transactions On Graphics (TOG), 38(5):1–12, Oct. 2019.
- Voxurf: Voxel-based efficient and accurate neural surface reconstruction. In International Conference on Learning Representations (ICLR), 2023.
- Gifs: Neural implicit function for general shape representation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
- MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstruction. In Neural Information Processing Systems (NeurIPS), 2022.
- Thingi10k: A dataset of 10,000 3d-printing models. arXiv preprint arXiv:1605.04797, 2016.
- https://vcg.isti.cnr.it/vcglib/.
- libigl: A simple C++ geometry processing library, 2018. https://libigl.github.io/.
- Practical quad mesh simplification. CGF, 29(2), 407–418, 2010.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.
