Few-Shot Single-View 3-D Object Reconstruction with Compositional Priors (2004.06302v2)

Abstract: The impressive performance of deep convolutional neural networks in single-view 3D reconstruction suggests that these models perform non-trivial reasoning about the 3D structure of the output space. However, recent work has challenged this belief, showing that complex encoder-decoder architectures perform similarly to nearest-neighbor baselines or simple linear decoder models that exploit large amounts of per category data in standard benchmarks. On the other hand settings where 3D shape must be inferred for new categories with few examples are more natural and require models that generalize about shapes. In this work we demonstrate experimentally that naive baselines do not apply when the goal is to learn to reconstruct novel objects using very few examples, and that in a \emph{few-shot} learning setting, the network must learn concepts that can be applied to new categories, avoiding rote memorization. To address deficiencies in existing approaches to this problem, we propose three approaches that efficiently integrate a class prior into a 3D reconstruction model, allowing to account for intra-class variability and imposing an implicit compositional structure that the model should learn. Experiments on the popular ShapeNet database demonstrate that our method significantly outperform existing baselines on this task in the few-shot setting.

Overview of Few-Shot Single-View 3D Object Reconstruction with Compositional Priors

The paper "Few-Shot Single-View 3D Object Reconstruction with Compositional Priors" presents a novel approach for single-view 3D object reconstruction within the context of few-shot learning. The research focuses on overcoming the limitations of standard deep learning models which, despite their remarkable success in various domains, struggle to generalize in scenarios where data for novel object classes is scarce.

Background and Motivation

Traditional deep CNNs have demonstrated efficacy in single-view 3D reconstruction tasks, heavily relying on large datasets to learn mappings from 2D images to 3D shapes. Recent findings challenge the assumption that these models understand the 3D structure in depth, showing that they can often rely on simple classification mechanisms rather than true structure reasoning. This exposes a significant limitation when facing few-shot learning scenarios, where models must extrapolate learned knowledge to new, unseen object classes from limited examples—an ability innate to human perception.

Proposed Methodology

The paper introduces three strategies to enhance 3D shape reconstruction in few-shot settings by employing compositional priors:

1. Global Class Embedding (GCE): In this strategy, the model learns a global embedding for each class, capturing the essence of the class structure by using all available shape examples. This approach leverages a shared global representation for various instances within a class, thus accounting for intra-class variability.
2. Compositional Global Class Embeddings (CGCE): This method extends GCE by integrating compositional principles. It decomposes class representations into linear combinations of shared vectors across classes, housed within several codebooks. This design promotes the sharing of common structural concepts across different classes, enhancing generalization.
3. Multi-scale Conditional Class Embeddings (MCCE): This approach introduces multi-scale conditioning in the 3D decoder through conditional batch normalization, whereby class-specific parameters are applied at multiple layers of the decoding process, fostering multi-scale shape understanding.

Analysis and Results

The researchers conducted experiments on the ShapeNet dataset and reported several key findings:

• The proposed GCE method significantly outperformed a zero-shot baseline by over 50% and surpassed the state-of-the-art by over 10% in relative performance, marking a substantial advancement in few-shot 3D reconstruction.
• CGCE showcased the model's capacity to generalize better across classes by leveraging shared codes, thereby improving the reconstruction quality profoundly in scenarios with multiple examples.
• MCCE further enhanced model performance by embedding class scale-specific features directly into the reconstruction process.

Implications and Future Directions

The implications of this paper are two-fold. Practically, it advances the capability of deep learning models to generalize from minimal data, aligning machine perception closer to human-like versatility in recognizing and understanding novel objects. Theoretically, it highlights the potential of compositional and multi-scale priors in enriching model architecture, suggesting that future work could extend these principles to explore more sophisticated 3D shape representations and higher-resolution reconstructions.

In conclusion, this research contributes significantly to the domain of few-shot learning and 3D reconstruction by addressing critical gaps in model generalization capabilities through innovative use of compositional priors. Future exploration could delve into integrating these approaches with other state-of-the-art techniques or investigating their efficacy in real-world applications beyond ShapeNet.