mlsanityData to 7 Oct 2026

D2IM-Net: Learning Detail Disentangled Implicit Fields from Single Images

arXiv 2012.06650 ↗First posted 11 Dec 2020cs.CVcited by 42

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Abstract (arXiv)

We present the first single-view 3D reconstruction network aimed at recovering geometric details from an input image which encompass both topological shape structures and surface features. Our key idea is to train the network to learn a detail disentangled reconstruction consisting of two functions, one implicit field representing the coarse 3D shape and the other capturing the details. Given an input image, our network, coined D2IM-Net, encodes it into global and local features which are respectively fed into two decoders. The base decoder uses the global features to reconstruct a coarse implicit field, while the detail decoder reconstructs, from the local features, two displacement maps, defined over the front and back sides of the captured object. The final 3D reconstruction is a fusion between the base shape and the displacement maps, with three losses enforcing the recovery of coarse shape, overall structure, and surface details via a novel Laplacian term.

Key facts. Cited by 42 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Dec 2020.

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Citing papers per year: 2021: 5; 2022: 9; 2023: 15; 2024: 7; 2025: 5; 2026: 1.

42 papers.

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  1. 2021Deep Marching Tetrahedra: a Hybrid Representation for High-Resolution 3D Shape Synthesiscited by 459
  2. 2022Neural Wavelet-domain Diffusion for 3D Shape Generationcited by 132
  3. 2022HF-NeuS: Improved Surface Reconstruction Using High-Frequency Detailscited by 120
  4. 2022Towards Implicit Text-Guided 3D Shape Generationcited by 68
  5. 2021Geometry-Consistent Neural Shape Representation with Implicit Displacement Fieldscited by 60
  6. 2022Neural Template: Topology-aware Reconstruction and Disentangled Generation of 3D Meshescited by 34
  7. 2023Neural Wavelet-domain Diffusion for 3D Shape Generation, Inversion, and Manipulationcited by 27
  8. 2022ANISE: Assembly-based Neural Implicit Surface rEconstructioncited by 16
  9. 2023Cross-domain Compositing with Pretrained Diffusion Modelscited by 13
  10. 2023Slice3D: Multi-Slice, Occlusion-Revealing, Single View 3D Reconstructioncited by 13
  11. 2023DeFormer: Integrating Transformers with Deformable Models for 3D Shape Abstraction from a Single Imagecited by 11
  12. 2021Object Wake-up: 3D Object Rigging from a Single Imagecited by 8
30 more
  1. 2022PatchRD: Detail-Preserving Shape Completion by Learning Patch Retrieval and Deformationcited by 7
  2. 2023Deformable Model-Driven Neural Rendering for High-Fidelity 3D Reconstruction of Human Heads Under Low-View Settingscited by 7
  3. 2023LIST: Learning Implicitly from Spatial Transformers for Single-View 3D Reconstructioncited by 6
  4. 2024SweepNet: Unsupervised Learning Shape Abstraction via Neural Sweeperscited by 5
  5. 2023Parameterization-driven Neural Surface Reconstruction for Object-oriented Editing in Neural Renderingcited by 4
  6. 2022VPFusion: Joint 3D Volume and Pixel-Aligned Feature Fusion for Single and Multi-view 3D Reconstructioncited by 3
  7. 2023A Review of Deep Learning-Powered Mesh Reconstruction Methodscited by 3
  8. 2023Attention-based Part Assembly for 3D Volumetric Shape Modelingcited by 3
  9. 2022ARO-Net: Learning Implicit Fields from Anchored Radial Observationscited by 2
  10. 2023Self-supervised Adaptive Pre-training of Multilingual Speech Models for Language and Dialect Identificationcited by 2
  11. 2021Neural Implicit 3D Shapes from Single Images with Spatial Patternscited by 0
  12. 2021Signal Transformer: Complex-valued Attention and Meta-Learning for Signal Recognitioncited by 0
  13. 2022Inferring Implicit 3D Representations from Human Figures on Pictorial Mapscited by 0
  14. 2023Analysis of Sampling Strategies for Implicit 3D Reconstructioncited by 0
  15. 2023Pix2Repair: Implicit Shape Restoration from Imagescited by 0
  16. 2023Enhancing Multilingual Speech Recognition through Language Prompt Tuning and Frame-Level Language Adaptercited by 0
  17. 2023GMTR: Graph Matching Transformerscited by 0
  18. 2023DarkShot: Lighting Dark Images with Low-Compute and High-Qualitycited by 0
  19. 2024TRLS: A Time Series Representation Learning Framework via Spectrogram for Medical Signal Processingcited by 0
  20. 2024TEN-GUARD: Tensor Decomposition for Backdoor Attack Detection in Deep Neural Networkscited by 0
  21. 2024T-Pixel2Mesh: Combining Global and Local Transformer for 3D Mesh Generation from a Single Imagecited by 0
  22. 2024Strategic Arms with Side Communication Prevail Over Low-Regret MAB Algorithmscited by 0
  23. 2024Towards General Deepfake Detection with Dynamic Curriculumcited by 0
  24. 2024Attr-Int: A Simple and Effective Entity Alignment Framework for Heterogeneous Knowledge Graphscited by 0
  25. 2025Realistic Clothed Human and Object Joint Reconstruction from a Single Imagecited by 0
  26. 2025X2BR: High-Fidelity 3D Bone Reconstruction from a Planar X-Ray Image with Hybrid Neural Implicit Methodscited by 0
  27. 2025Mask6D: Masked Pose Priors For 6D Object Pose Estimationcited by 0
  28. 2025HPR3D: Hierarchical Proxy Representation for High-Fidelity 3D Reconstruction and Controllable Editingcited by 0
  29. 2025Radar2Shape: 3D Shape Reconstruction from High-Frequency Radar using Multiresolution Signed Distance Functionscited by 0
  30. 2026Appearance-Preserving Refinement of Generated 3D Assets for Monochromatic Fabricationcited by 0