Local Implicit Grid Representations for 3D Scenes (2003.08981v1)

Abstract: Shape priors learned from data are commonly used to reconstruct 3D objects from partial or noisy data. Yet no such shape priors are available for indoor scenes, since typical 3D autoencoders cannot handle their scale, complexity, or diversity. In this paper, we introduce Local Implicit Grid Representations, a new 3D shape representation designed for scalability and generality. The motivating idea is that most 3D surfaces share geometric details at some scale -- i.e., at a scale smaller than an entire object and larger than a small patch. We train an autoencoder to learn an embedding of local crops of 3D shapes at that size. Then, we use the decoder as a component in a shape optimization that solves for a set of latent codes on a regular grid of overlapping crops such that an interpolation of the decoded local shapes matches a partial or noisy observation. We demonstrate the value of this proposed approach for 3D surface reconstruction from sparse point observations, showing significantly better results than alternative approaches.

• The paper presents Local Implicit Grids (LIG) as a method for reconstructing 3D scenes from sparse data by leveraging part-based shape embeddings.
• It divides the space into overlapping local regions encoded with latent codes to capture fine geometric details while ensuring global continuity.
• Optimization of these latent codes significantly improves reconstruction metrics, demonstrating enhanced efficiency and generalizability across varied scenes.

Overview of "Local Implicit Grid Representations for 3D Scenes"

The paper "Local Implicit Grid Representations for 3D Scenes" introduces an innovative approach to 3D shape representation, termed Local Implicit Grids (LIG), which is particularly designed for scalability and generality in reconstructing 3D surfaces from sparse point data. This representation effectively balances the need for scalability across large scenes with the necessity of capturing fine geometric details. The authors propose a method to learn and leverage geometric priors through a part-based embedding strategy, offering significant improvements over traditional methods in 3D reconstruction tasks.

Methodology and Key Contributions

The research presents a systematic approach to address challenges in 3D scene reconstruction:

1. Part-Based Shape Embedding: The authors contend that while global shape features vary significantly across objects and scenes, local geometric features exhibit commonality at an intermediate scale (neither too large nor too small). Consequently, they train an autoencoder on local parts of 3D shapes derived from the ShapeNet dataset. The encoder captures these intermediate-scale features, and the decoder reconstructs them from a sparse point set, offering greater versatility across different object categories.
2. Local Implicit Grid Representation: The paper introduces the LIG representation, which divides the space into a grid of overlapping part-sized local regions. Each region is represented by a latent code, which can be interpolated to decode the implicit surface. This method ensures continuity and enhances the expressiveness of the reconstructed geometry.
3. Optimization for Scene Reconstruction: For practical use, particularly in scene reconstruction from sparse data, the authors develop an optimization approach that adjusts the latent codes in the LIG grid to fit the observed data. The resultant framework can effectively reconstruct complex scenes with high geometric fidelity without requiring extensive, scene-level datasets for training.

Results and Implications

The empirical analysis demonstrates that the LIG representation significantly outperforms existing methods in reconstructing 3D surfaces from sparse point clouds, achieving notable improvements in Chamfer Distance and F-Score metrics. The results underscore the model's exceptional ability to generalize learned geometric features to novel object categories and scenes not encountered during training. For example, in the Matterport scene reconstruction task, the method attained an F-Score of 0.889 compared to 0.455 for the Poisson Surface Reconstruction benchmark.

Practical and Theoretical Implications

The proposed approach holds substantial implications for both the theoretical understanding of geometric representations in AI and the practical deployment of 3D reconstruction systems:

• Scalability and Efficiency: By focusing on localized geometric features independent of global object categories, the LIG representation can scale efficiently to large scenes, offering a compelling alternative to approaches that require comprehensive global shape encoding.
• Generalizability: The method's capacity to leverage shape priors learned from object parts in constructing entire scenes demonstrates an advancement in generalizable AI models. This is particularly beneficial for real-world applications where acquiring extensive training datasets for varied scenes is infeasible.
• Future Research Directions: This approach opens avenues for exploring other spatial partitioning strategies or different function-based representations (such as Occupancy Networks) to further enhance the model's capability in diverse settings.

In conclusion, the paper makes a substantial contribution to the field of 3D scene reconstruction. By employing a novel grid-based implicit representation, it adeptly balances local and global geometric features, paving the way for further development in scalable and flexible 3D reconstruction techniques. Future work might delve into optimizing the training process to encompass even broader environments while maintaining computational efficiency.