Source-linked AI summary
Learning Implicit Fields for Generative Shape Modeling
Zhiqin Chen, Hao Zhang
TL;DR
Existing 3D shape generators often produce low-resolution, overly smooth or discontinuous surfaces with topological noise. The paper introduces IM-NET, a point-coordinate implicit decoder integrated into autoencoding and generative frameworks, and reports cleaner meshes, improved interpolation, and superior visual quality, while noting training and sampling costs plus limits on topology regulation and multicategory generalization.
Problem
Existing 3D shape generators remain limited by low-resolution outputs, overly smoothed or discontinuous surfaces, and topological noise.
Method
IM-NET predicts pointwise inside/outside status from spatial coordinates and shape features, replacing conventional decoders in autoencoders and GANs.
Results
The resulting models produce cleaner closed meshes, superior visual quality, and cleaner interpolation results, including topology changes.
Takeaways & Limitations
Implicit decoding provides a generic decoder that can support shape autoencoding, generation, interpolation, and single-view reconstruction.
Takeaways & Limitations
The method requires substantially longer training and resolution-dependent generation, lacks regulated topology evolution and multicategory generalization, and can introduce low-frequency shape errors.
Abstract
from arXiv · showhide
We advocate the use of implicit fields for learning generative models of shapes and introduce an implicit field decoder, called IM-NET, for shape generation, aimed at improving the visual quality of the generated shapes. An implicit field assigns a value to each point in 3D space, so that a shape can be extracted as an iso-surface. IM-NET is trained to perform this assignment by means of a binary classifier. Specifically, it takes a point coordinate, along with a feature vector encoding a shape, and outputs a value which indicates whether the point is outside the shape or not. By replacing conventional decoders by our implicit decoder for representation learning (via IM-AE) and shape generation (via IM-GAN), we demonstrate superior results for tasks such as generative shape modeling, interpolation, and single-view 3D reconstruction, particularly in terms of visual quality. Code and supplementary material are available at https://github.com/czq142857/implicit-decoder.
1. Introduction
Existing 3D shape generators often produce low-resolution, overly smooth or discontinuous surfaces with topological noise. The paper introduces IM-NET, an implicit decoder that learns pointwise inside/outside assignments to improve generated-shape visual quality.
- State-of-the-art 3D shape generators still suffer from low resolution, smoothed or discontinuous surfaces, and topological irregularities.
- IM-NET takes point coordinates and shape features, then predicts whether each point lies inside or outside the shape.It is trained as a binary classifier and follows an encoder that produces shape feature vectors.
- Unlike voxel CNN decoders, IM-NET learns shape boundaries rather than voxel distributions over a bounded volume.This boundary-based formulation is presented as a mechanism for improving visual quality.
- The decoder can be sampled at arbitrary resolution beyond the resolution of the training shapes.Figure 1 describes outputs sampled at 512^3 resolution from models trained on 64^3 or 128^3 voxelized shapes.
- IM-NET produces cleaner interpolation results than previous works, including interpolations involving topological changes.The paper attributes this to learning inside/outside assignments that directly define shape evolution.
- Replacing conventional decoders with IM-NET yields IM-AEs and IM-GANs for representation learning, generation, interpolation, and single-view reconstruction.Experiments report superiority over previous works, particularly in visual quality.
2. Related work
Prior 3D shape-learning methods use diverse representations, including voxels, point clouds, patches, and structural graphs, each with distinct reconstruction or visual-quality limitations. This work positions implicit fields as a continuous alternative for generative shape modeling.
- Deep shape-learning methods have used voxel grids, octrees, multi-view images, point clouds, geometry images, deformable patches, and structural graphs.
- The paper introduces a deep network for learning implicit fields for generative shape modeling.
- Earlier generative approaches learned voxel embeddings, generated voxel shapes with GANs, or used latent-GAN workflows with autoencoders.
- IM-NET assigns inside/outside status using spatial point coordinates, learning a continuous implicit function over space.This distinguishes it from another binary-classification approach that does not use spatial point coordinates in the same way.
- Point-cloud methods are lightweight but do not directly provide surface or topological information, making reconstruction challenging.A cited marching-cubes approach still produces low-resolution meshes with common CNN-based issues.
- Template-deformation methods can produce artifacts such as gaps, foldovers, and overlaps when integrating multiple patches.Pixel2Mesh results are also described as tending toward excessive smoothing.
3. Implicit decoder and shape generation
The implicit decoder represents shapes as continuous point-wise fields and learns their inside/outside labels with a neural network. IM-NET supports shape analysis and synthesis applications, using sampled training points, weighted loss, and framework-specific encoders or latent GANs.
- Implicit field representation: An implicit field is a continuous function over space whose iso-surface reconstructs a mesh shape.The paper uses Marching Cubes to recover the mesh from an appropriate field threshold.
- IM-NET decoder: IM-NET learns the inside/outside field by mapping sampled 3D points to predicted labels with an MLP parameterization.The model approximates F(p) continuously across space, enabling iso-surface extraction.
- Training data and loss: Surface-focused sampling reduces training points from n^3 voxel centers to roughly O(n^2) points while compensating with inverse-density weights.Sampling concentrates points near shape surfaces and assigns each point a weight based on local sampling density.
- Training data and loss: IM-NET is trained with a weighted mean squared error between ground-truth and predicted labels for sampled points.The weights account for the sampling distribution over the point set S.
- Applications: Reconstructed 3D meshes are obtained with Marching Cubes, while 2D outputs use grid sampling followed optionally by thresholding.These procedures convert the learned implicit field into mesh or binarized image representations.
4. Results and evaluation
Across reconstruction, generation, interpolation, and single-view reconstruction, IM-NET-based models are evaluated against conventional and state-of-the-art methods using qualitative and quantitative measures. The results emphasize improved visual quality, surface coherence, and interpolation behavior, while also exposing limits of common metrics and competing representations.
- 4.2. Auto-encoding 3D shapes: IM-AE produces visually better reconstructions than CNN-AE despite CNN-AE scoring better on MSE, IoU, and CD in nearly all categories.The comparison uses common reconstruction metrics and visual examination, with LFD argued to better reflect visual similarity.
- 4.3. 3D shape generation and interpolation: IM-GAN produces cleaner interpolation sequences with smooth part movements and topology changes, whereas CNN-based methods can exhibit missing or redundant parts.The interpolation comparison samples all models at 64^3, separating surface smoothness from sampling resolution.
- 4.2. Auto-encoding 3D shapes: 256^3 sampling reveals finer details, including a table board thinner than the training resolution, demonstrating resolution-flexible implicit-field decoding.IM-AE64 and IM-AE256 use the same model with different sampling grids, whereas CNN-AE has fixed 64^3 output size.
- 4.3. 3D shape generation and interpolation: IM-GAN performs better on both COV-LFD and MMD-LFD and generates smoother, more coherent surfaces than the compared generation methods.The comparison includes CNN-GAN, 3DGAN, and PC-GAN; 3DGAN shows mode collapse, while PC-GAN lacks detailed features.
- 4.5. Single-view 3D reconstruction (SVR): In single-view reconstruction, IM-SVR is compared with HSP and AtlasNet, whose outputs involve distinct trade-offs in detail recovery, patch artifacts, and topology.AtlasNet25 can show slits, foldovers, or overlaps; AtlasNetO is constrained by sphere topology; HSP produces smooth surfaces but misses most details.
5. Conclusion, limitation, and future work
IM-NET is a generic implicit field decoder that supports shape autoencoding, generation, interpolation, and single-view reconstruction, producing cleaner meshes and improved topology handling. Its costs include substantially longer training and resolution-dependent generation, while meaningful multicategory morphing remains unresolved and low-frequency errors can occur.
- Conclusion: IM-NET plugs into contemporary networks for autoencoding, generation, interpolation, and single-view reconstruction, producing cleaner closed meshes and better topology handling during interpolation.
- Limitation: Including point coordinates improves shape-boundary learning but requires applying the decoder at every training point, substantially increasing training time.CNN-AE is typically 30 times faster than IM-AE without progressive training and 15 times faster with it.
- Limitation: Shape generation becomes slower as sampling resolution increases because IM-NET must evaluate every point in the voxel grid.
- Limitation: Meaningful topology evolution between highly dissimilar shapes is not yet regulated, and the model is currently trained separately for each shape category.
- Limitation: The method can produce greater visual quality while introducing more low-frequency errors such as global thinning or thickening.
- Future work: Future work includes more efficient decoder structures, outputs for attributes such as color and signed distance, and generalization of IM-NET.