FineRecon: Depth-aware Feed-forward Network for Detailed 3D Reconstruction (2304.01480v2)

Abstract: Recent works on 3D reconstruction from posed images have demonstrated that direct inference of scene-level 3D geometry without test-time optimization is feasible using deep neural networks, showing remarkable promise and high efficiency. However, the reconstructed geometry, typically represented as a 3D truncated signed distance function (TSDF), is often coarse without fine geometric details. To address this problem, we propose three effective solutions for improving the fidelity of inference-based 3D reconstructions. We first present a resolution-agnostic TSDF supervision strategy to provide the network with a more accurate learning signal during training, avoiding the pitfalls of TSDF interpolation seen in previous work. We then introduce a depth guidance strategy using multi-view depth estimates to enhance the scene representation and recover more accurate surfaces. Finally, we develop a novel architecture for the final layers of the network, conditioning the output TSDF prediction on high-resolution image features in addition to coarse voxel features, enabling sharper reconstruction of fine details. Our method, FineRecon, produces smooth and highly accurate reconstructions, showing significant improvements across multiple depth and 3D reconstruction metrics.

• The paper introduces resolution-agnostic TSDF supervision to capture fine geometric details in 3D reconstructions.
• It employs novel multi-view depth guidance to refine feature volumes, boosting performance on metrics like Chamfer distance and F1 scores.
• The innovative architecture combines coarse voxel and high-resolution image features, enabling efficient and detailed 3D scene recovery.

FineRecon: Enhancing 3D Scene Reconstruction with Depth-aware Networks

FineRecon represents a significant advancement in the area of 3D scene reconstruction from posed images, a field with broad applicability in domains such as autonomous navigation and virtual asset creation. The paper outlines a considerable shift towards improving the fidelity of 3D reconstructions by integrating innovative techniques into a feed-forward neural network architecture.

Methodological Innovations

The authors identify key limitations in existing 3D reconstruction approaches, primarily the inability to capture fine geometric details due to the coarse resolution of the truncated signed distance function (TSDF) typically employed. FineRecon addresses these challenges through three primary innovations:

1. Resolution-agnostic TSDF Supervision: By eliminating interpolation errors inherent in traditional TSDF alignment strategies, this new method directly supervises the model at precise locations where ground-truth data is known. This approach minimizes detail corruption, enabling high-fidelity learning signals during the training phase.
2. Depth Guidance Using Multi-view Estimates: FineRecon integrates a novel depth guidance strategy that refines feature volumes with multi-view depth estimates. This step significantly enhances surface representation quality, offering substantial improvements across various geometric metrics. The integration of depth estimates offers structural guidance to the CNN, allowing for more accurate surface recovery.
3. Innovative TSDF Prediction Architecture: The paper introduces a new architectural design for TSDF prediction within the network, conditioned not only on coarse voxel features but also on high-resolution image features. This facilitates the reconstruction of intricate details, enabling sub-voxel level detail capture without the need for retraining at different resolutions.

Experimental Evaluation

The effectiveness of FineRecon is demonstrated through comprehensive evaluations on the ScanNet dataset. The method consistently outperforms prior works by achieving the best-in-class results across critical 3D mesh and 2D depth metrics like Chamfer distance and F1 scores, thereby affirming its robustness in complex scene reconstructions. Notably, the model's ability to achieve state-of-the-art performance without computationally demanding test-time optimization underscores its practical relevance.

Implications and Future Directions

FineRecon's methodological contributions have significant implications for both theoretical exploration and practical applications. The introduction of accurate TSDF supervision can be expanded in future research to exploit adaptive sampling techniques, potentially leading to even greater efficiency in neural 3D reconstruction frameworks. Moreover, the depth guidance mechanism provides a promising avenue for integrating geometric priors into neural architectures, with potential applications in rendering and object interaction within reconstructed environments.

The paper acknowledges certain limitations, such as missed local structures and computational costs associated with dense feature volumes. Addressing these challenges in future work, perhaps through hybrid methods incorporating iterative optimization or augmenting sparse convolutions, could enhance both accuracy and efficiency.

In conclusion, FineRecon marks a meaningful progression in 3D reconstruction technology, achieving substantial improvements in detail accuracy and computational efficiency. The techniques introduced hold promising opportunities for broadening understanding and capabilities within the field, providing a versatile platform for future innovations in scene reconstruction technologies.