mlsanityData to 14 Oct 2026

HybridSDF: Combining Deep Implicit Shapes and Geometric Primitives for 3D Shape Representation and Manipulation

arXiv 2109.10767 ↗First posted 22 Sep 2021cs.CV cs.AI cs.CGcited by 5

Not on the map. Why?

Abstract (arXiv)

Deep implicit surfaces excel at modeling generic shapes but do not always capture the regularities present in manufactured objects, which is something simple geometric primitives are particularly good at. In this paper, we propose a representation combining latent and explicit parameters that can be decoded into a set of deep implicit and geometric shapes that are consistent with each other. As a result, we can effectively model both complex and highly regular shapes that coexist in manufactured objects. This enables our approach to manipulate 3D shapes in an efficient and precise manner.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 33 of its 46 references that are in the map. First posted on arXiv 22 Sep 2021.

Builds on

33 of 46 references.

References · 33

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  3. 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,897
  4. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,689
  5. 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,041
  6. 2018Learning Implicit Fields for Generative Shape Modelingcited by 1,060
  7. 2020Convolutional Occupancy Networkscited by 761
  8. 20163D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstructioncited by 588
  9. 2020Implicit Functions in Feature Space for 3D Shape Reconstruction and Completioncited by 343
  10. 2019DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstructioncited by 333
  11. 2019Learning Shape Templates with Structured Implicit Functionscited by 242
  12. 2016Learning Shape Abstractions by Assembling Volumetric Primitivescited by 181
21 more
  1. 2019CvxNet: Learnable Convex Decompositioncited by 174
  2. 2019Superquadrics Revisited: Learning 3D Shape Parsing beyond Cuboidscited by 124
  3. 2020MeshSDF: Differentiable Iso-Surface Extractioncited by 112
  4. 2021Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networkscited by 103
  5. 2020DualSDF: Semantic Shape Manipulation using a Two-Level Representationcited by 82
  6. 20173D-PRNN: Generating Shape Primitives with Recurrent Neural Networkscited by 77
  7. 2020Learning Unsupervised Hierarchical Part Decomposition of 3D Objects from a Single RGB Imagecited by 72
  8. 2021Deep Implicit Moving Least-Squares Functions for 3D Reconstructioncited by 67
  9. 20163D Shape Segmentation with Projective Convolutional Networkscited by 63
  10. 2018Im2Struct: Recovering 3D Shape Structure from a Single RGB Imagecited by 55
  11. 2021Sketch2Mesh: Reconstructing and Editing 3D Shapes from Sketchescited by 51
  12. 20213D Shape Generation with Grid-based Implicit Functionscited by 48
  13. 2017Imposing Hard Constraints on Deep Networks: Promises and Limitationscited by 43
  14. 2018Geodesic Convolutional Shape Optimizationcited by 37
  15. 2019Deep Parametric Shape Predictions using Distance Fieldscited by 30
  16. 2020Learning Generative Models of Shape Handlescited by 25
  17. 2021CPFN: Cascaded Primitive Fitting Networks for High-Resolution Point Cloudscited by 24
  18. 2021Cuboids Revisited: Learning Robust 3D Shape Fitting to Single RGB Imagescited by 17
  19. 2021DeepMesh: Differentiable Iso-Surface Extractioncited by 14
  20. 2016FusionNet: 3D Object Classification Using Multiple Data Representationscited by 12
  21. 2020UCLID-Net: Single View Reconstruction in Object Spacecited by 4

Built on by

Cited by year

Citing papers per year: 2023: 4; 2024: 0; 2025: 1.

5 papers.

  1. 2023Point2CAD: Reverse Engineering CAD Models from 3D Point Cloudscited by 36
  2. 2023Marching-Primitives: Shape Abstraction from Signed Distance Functioncited by 19
  3. 2023DPF-Net: Combining Explicit Shape Priors in Deformable Primitive Field for Unsupervised Structural Reconstruction of 3D Objectscited by 10
  4. 2023SURFSUP: Learning Fluid Simulation for Novel Surfacescited by 3
  5. 2025GLUE: Coordinating Pre-Trained Generative Models for System-Level Designcited by 0