VolumeFusion: Deep Depth Fusion for 3D Scene Reconstruction (2108.08623v1)

Abstract: To reconstruct a 3D scene from a set of calibrated views, traditional multi-view stereo techniques rely on two distinct stages: local depth maps computation and global depth maps fusion. Recent studies concentrate on deep neural architectures for depth estimation by using conventional depth fusion method or direct 3D reconstruction network by regressing Truncated Signed Distance Function (TSDF). In this paper, we advocate that replicating the traditional two stages framework with deep neural networks improves both the interpretability and the accuracy of the results. As mentioned, our network operates in two steps: 1) the local computation of the local depth maps with a deep MVS technique, and, 2) the depth maps and images' features fusion to build a single TSDF volume. In order to improve the matching performance between images acquired from very different viewpoints (e.g., large-baseline and rotations), we introduce a rotation-invariant 3D convolution kernel called PosedConv. The effectiveness of the proposed architecture is underlined via a large series of experiments conducted on the ScanNet dataset where our approach compares favorably against both traditional and deep learning techniques.

• The paper introduces a two-stage pipeline that first computes local depth maps with deep multi-view stereo techniques and then fuses them into a coherent 3D model.
• It proposes a novel PosedConv layer to achieve rotation-invariant feature matching, enhancing reconstruction accuracy across diverse viewpoints.
• Extensive experiments on the ScanNet dataset demonstrate improved depth and geometry metrics over traditional methods, underlining its practical impact.

Overview of VolumeFusion: Deep Depth Fusion for 3D Scene Reconstruction

This paper, VolumeFusion: Deep Depth Fusion for 3D Scene Reconstruction, introduces a novel framework that seeks to enhance the accuracy and interpretability of 3D scene reconstruction from multiple views using deep neural networks. The authors propose a two-stage pipeline approach that mirrors traditional multi-view stereo methods: local depth map computation followed by global depth map fusion. This dual-stage architecture not only provides a structured approach to scene reconstruction but also leverages the strengths of deep learning techniques for improved results.

Key Contributions

The paper emphasizes several innovations within this two-stage framework:

1. Deep Multi-View Stereo (MVS) Technique: The initial stage involves the computation of local depth maps using advanced multi-view stereo techniques supported by deep neural networks. This process aims to leverage local photometric consistency between overlapping image frames.
2. Depth Maps and Image Features Fusion: The subsequent fusion stage integrates depth maps with image features to construct a single Truncated Signed Distance Function (TSDF) volume, a process crucial for achieving a coherent 3D reconstruction.
3. PosedConv Layer: A novel rotation-invariant 3D convolution kernel, termed PosedConv, is introduced to enhance matching performance between images captured from varying viewpoints, including wide baselines and significant rotations. This improves the robustness of the depth fusion process and facilitates more globally consistent volumetric representation.

Experimental Findings

The effectiveness of the proposed methods is validated through extensive experiments conducted on the ScanNet dataset. The results indicate that the VolumeFusion method outperforms traditional techniques and previous deep learning-based methods, both in depth evaluation and 3D geometry reconstruction.

Metrics: The prescribed quantitative metrics—AbsRel, AbsDiff, SqRel, RMSE for depth evaluation and $\mathcal{L}_{1}$, Acc, Comp, F-score for 3D geometry—demonstrate the superiority of this approach in enhancing reconstruction accuracy and addressing complex scene structures such as hallways and corners.

Implications and Future Prospects

The implications of this research are manifold. Practically, it advances the development of more efficient 3D reconstruction systems capable of operating effectively across various environments, an asset in fields like robotics and virtual reality. Theoretically, it presents significant advancements by demonstrating the potential of hybrid approaches that integrate traditional modeling techniques with deep learning.

Looking ahead, the authors speculate future research directions which could include optimized strategies for volumetric representation that require less computational power while maintaining high-resolution outputs. Another potential area of exploration is the application of the framework in real-time dynamic scenarios. Further exploration of the volume-free fusion approaches may also offer actionable insights into achieving scalable and efficient 3D scene reconstruction systems.

In summary, this paper provides a sophisticated methodological approach to 3D reconstruction, integrating the interpretability benefits of traditional methods with the precision offered by deep learning advancements, setting the stage for continued development in artificial intelligence and computer vision realms.