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Few-Shot Unsupervised Implicit Neural Shape Representation Learning with Spatial Adversaries
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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.
Forward citations
Cited by 4 Pith papers
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Sparfels reconstructs accurate 3D shape and novel views from sparse, unposed images in minutes by bootstrapping 2D Gaussian splatting with MASt3R and a new splatted-color-variance loss.
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Toward Robust Neural Reconstruction from Sparse Point Sets
A Sinkhorn-regularized DRO training loss improves neural SDF reconstruction from sparse noisy point clouds compared to recent baselines.
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SparSplat: Fast Multi-View Reconstruction with Generalizable 2D Gaussian Splatting
A feed-forward model regressing 2D Gaussian splat parameters from three views reports the best Chamfer distance on DTU sparse reconstruction in its comparison table, and competitive novel view synthesis, at roughly 80...
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