LAMP: Data-Efficient Linear Affine Weight-Space Models for Parameter-Controlled 3D Shape Generation and Extrapolation
read the original abstract
Generating high-fidelity 3D geometries under explicit parameter constraints is central to engineering design, yet current methods often require large datasets and fail to provide reliable control beyond the training distribution. We introduce LAMP, a data-efficient framework for controllable and interpretable 3D generation that aligns signed distance function (SDF) decoders by overfitting each exemplar from a shared initialization, then generates new designs by solving a parameter-constrained affine mixing problem in the aligned weight space. To improve reliability, we propose a linearity-mismatch safety metric that detects when mixed decoders leave the valid local regime. We evaluate LAMP on DrivAerNet++, BlendedNet, and additional industry-level vehicle families, including sports cars, SUVs, and convertibles. LAMP enables controlled interpolation with as few as 50 samples, safe extrapolation up to 100% beyond training ranges, and performance-guided optimization under fixed parameters, outperforming conditional autoencoder and Deep Network Interpolation (DNI) baselines in extrapolation, data efficiency, and parameter fidelity. Our results demonstrate that LAMP advances controllable, data-efficient, and safe 3D generation for design exploration, dataset generation, and performance-driven optimization.
This paper has not been read by Pith yet.
Forward citations
Cited by 5 Pith papers
-
Geometry-Aware Neural Optimizer for Shape Optimization and Inversion
GANO unifies shape encoding with auto-decoders, denoising-stabilized latent optimization, and geometry-injected surrogates into an end-to-end differentiable pipeline for PDE-governed shape optimization and inversion.
-
Geometry-Aware Neural Optimizer for Shape Optimization and Inversion
GANO is an end-to-end differentiable latent-space optimizer that unifies shape encoding, surrogate prediction, and controllable geometry updates for PDE-governed shape optimization and inversion.
-
Geometry-Aware Neural Optimizer for Shape Optimization and Inversion
GANO unifies shape encoding, field prediction, and latent optimization with denoising for stable, controllable updates in PDE shape problems, reporting SOTA accuracy and up to 55.9% lift-to-drag gains on benchmarks.
-
GeoFunFlow-3D: A Physics-Guided Generative Flow Matching Framework for High-Fidelity 3D Aerodynamic Inference over Complex Geometries
GeoFunFlow-3D reduces pressure-field RRMSE to 0.0215 on industrial 3D datasets by combining flow matching with physics-guided components that target spectral bias and localized shock structures.
-
FLARE: A Data-Efficient Surrogate for Predicting Displacement Fields in Directed Energy Deposition
FLARE predicts post-cooling displacement fields in directed energy deposition by encoding simulations as implicit neural fields whose weights are regularized to follow an affine structure in parameter space, enabling ...
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.