VolRecon: Volume Rendering of Signed Ray Distance Functions for Generalizable Multi-View Reconstruction (2212.08067v2)

Abstract: The success of the Neural Radiance Fields (NeRF) in novel view synthesis has inspired researchers to propose neural implicit scene reconstruction. However, most existing neural implicit reconstruction methods optimize per-scene parameters and therefore lack generalizability to new scenes. We introduce VolRecon, a novel generalizable implicit reconstruction method with Signed Ray Distance Function (SRDF). To reconstruct the scene with fine details and little noise, VolRecon combines projection features aggregated from multi-view features, and volume features interpolated from a coarse global feature volume. Using a ray transformer, we compute SRDF values of sampled points on a ray and then render color and depth. On DTU dataset, VolRecon outperforms SparseNeuS by about 30% in sparse view reconstruction and achieves comparable accuracy as MVSNet in full view reconstruction. Furthermore, our approach exhibits good generalization performance on the large-scale ETH3D benchmark.

• The paper introduces VolRecon, a novel framework using Signed Ray Distance Functions (SRDF) for accurate generalizable implicit 3D scene reconstruction.
• VolRecon combines global 3D volume features and projection-based multi-view features, using a ray transformer to refine SRDF values for enhanced detail.
• VolRecon demonstrates improved generalization (e.g., 30% better sparse view reconstruction than SparseNeuS) and finer detail reconstruction compared to previous methods.

VolRecon: Volume Rendering of Signed Ray Distance Functions for Generalizable Multi-View Reconstruction

The paper introduces VolRecon, a new framework for achieving accurate generalizable implicit 3D scene reconstruction through the application of Signed Ray Distance Functions (SRDF). This framework is seen as a significant extension upon the methodologies reliant on Neural Radiance Fields (NeRF), addressing the challenges in transferring learned priors across different scenes without requiring per-scene optimization.

Overview and Methodology

VolRecon stands out by incorporating SRDF to enhance the fidelity and detail in reconstructed scenes compared to previous methods. It leverages two main types of features: projection features sourced from multi-view images and volume features derived from a global 3D feature volume. A ray transformer further refines SRDF values by using a sequence-based approach to capture both local and non-local geometric details along the viewing rays.

1. Global Feature Volume: This construct allows VolRecon to encapsulate spatial information beyond what individual view images can offer, providing shape priors based on aggregated voxel features.
2. Projection-Based Feature Aggregation: Using view transformers, VolRecon effectively synthesizes information across multiple images, reducing radiance-geometry ambiguity and enabling robust feature matching even in scenes with occlusions or textureless regions.
3. Ray Transformer: This component processes features sampled along a ray in sequence, significantly aiding in determining surface intersection points along specific viewing directions thanks to its attention mechanism.

Results and Comparisons

VolRecon's performance is compared against a suite of models, including SparseNeuS, MVSNet, IDR, VolSDF, and others both in terms of sparse view reconstruction and large-scale dataset generalization:

• Improved Generalization: VolRecon demonstrates increased adaptability to unseen scenes, outperforming SparseNeuS by 30% in sparse view reconstruction tasks on the DTU dataset, with results comparable to MVSNet in full view scenarios.
• Fine Detail and Noise Reduction: The proposed combination of local projection features with interpolated global volume features enables VolRecon to maintain finer details and sharper boundaries compared to competing methods, particularly noticeable when reconstructing surfaces with complex geometries and minimal occlusion.
• Dense Reconstruction Capabilities: In terms of rendering depth and completeness, the model exhibits superior performance metrics compared to traditional MVS baselines, showcasing the robustness in depth prediction and point cloud fusion.

Implications and Future Directions

VolRecon's approach offers notable practical implications for applications in robotics, augmented reality (AR), and virtual reality (VR), where seamless scene reproduction can enhance interactive experiences and automation processes. The framework's scalability presents a promising avenue for real-time applications if computational efficiency can be further optimized, such as through advancements in ray-transformer architectures or spatial partition strategies.

Future research may focus on applying progressive scene reconstruction techniques and further scaling the parameterization of feature volumes without adverse memory overhead. Expanding the framework to accommodate continuous learning and adaptation across dynamically evolving environments remains an attractive goal that could redefine applications in intelligent systems and 3D content generation.

In conclusion, VolRecon demonstrates notable advancements in generalizable multi-view reconstruction driven by SRDF, setting a foundational step forward for neural implicit representations and their capabilities in real-world applications.