Feed-Forward 3D Scene Modeling: A Problem-Driven Perspective
Reconstructing 3D representations from 2D inputs is a fundamental task in computer vision and graphics, serving as a cornerstone for understanding and interacting with the physical world. While traditional methods achieve high fidelity, they are limited by slow per-scene optimization or category-specific training, which hinders their practical deployment and scalability.
Hence, generalizable feed-forward 3D reconstruction has witnessed rapid development in recent years. By learning a model that maps images directly to 3D representations in a single forward pass, these methods enable efficient reconstruction and robust cross-scene generalization.
Key Observations and Research Directions
Our survey is motivated by a critical observation: despite the diverse geometric output representations, ranging from implicit fields to explicit primitives, existing feed-forward approaches share similar high-level architectural patterns. These patterns include:
- Image feature extraction backbones
- Multi-view information fusion mechanisms
- Geometry-aware design principles
Consequently, we abstract away from these representation differences and instead focus on model design. We propose a novel taxonomy centered on model design strategies that are agnostic to the output format. This proposed taxonomy organizes research directions into five key problems that drive recent research development:
- Feature Enhancement: Strategies to improve the representation of features extracted from input images.
- Geometry Awareness: Incorporating principles of geometry into model training and architecture to improve 3D fidelity.
- Model Efficiency: Techniques to reduce computational load while maintaining performance.
- Augmentation Strategies: Methods to enhance training datasets to improve model robustness and generalization.
- Temporal-Aware Models: Incorporating time as a factor in modeling to handle dynamic scenes and changes.
Empirical Grounding and Standardized Evaluation
To support this taxonomy with empirical grounding and standardized evaluation, we further comprehensively review related benchmarks and datasets. This review is essential for establishing a framework that can consistently evaluate the performance of various feed-forward 3D modeling techniques.
Additionally, we extensively discuss and categorize real-world applications based on feed-forward 3D models. These applications span various fields, including augmented reality, robotics, and virtual simulations, demonstrating the versatility and importance of effective 3D reconstruction methods.
Future Directions
Finally, we outline future directions to address open challenges such as scalability, evaluation standards, and world modeling. As the field progresses, addressing these challenges will be critical in enhancing the practicality and effectiveness of feed-forward 3D scene modeling techniques.