mlsanityData to 14 Oct 2026

Few-Shot Unsupervised Implicit Neural Shape Representation Learning with Spatial Adversaries

arXiv 2408.15114 ↗First posted 27 Aug 2024cs.CV cs.AI cs.GRcited by 5

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Abstract (arXiv)

Implicit Neural Representations have gained prominence as a powerful framework for capturing complex data modalities, encompassing a wide range from 3D shapes to images and audio. Within the realm of 3D shape representation, Neural Signed Distance Functions (SDF) have demonstrated remarkable potential in faithfully encoding intricate shape geometry. However, learning SDFs from sparse 3D point clouds in the absence of ground truth supervision remains a very challenging task. While recent methods rely on smoothness priors to regularize the learning, our method introduces a regularization term that leverages adversarial samples around the shape to improve the learned SDFs. Through extensive experiments and evaluations, we illustrate the efficacy of our proposed method, highlighting its capacity to improve SDF learning with respect to baselines and the state-of-the-art using synthetic and real data.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 56 of its 86 references that are in the map. First posted on arXiv 27 Aug 2024.

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56 of 86 references.

References · 56

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38 more
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Cited by year

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

5 papers.

  1. 2025Sparfels: Fast Reconstruction from Sparse Unposed Imagerycited by 8
  2. 2025SparSplat: Fast Multi-View Reconstruction with Generalizable 2D Gaussian Splattingcited by 7
  3. 2025Anti-Aliased 2D Gaussian Splattingcited by 7
  4. 2024Toward Robust Neural Reconstruction from Sparse Point Setscited by 5
  5. 2025TextureSplat: Per-Primitive Texture Mapping for Reflective Gaussian Splattingcited by 3