MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstruction (2206.00665v2)

Abstract: In recent years, neural implicit surface reconstruction methods have become popular for multi-view 3D reconstruction. In contrast to traditional multi-view stereo methods, these approaches tend to produce smoother and more complete reconstructions due to the inductive smoothness bias of neural networks. State-of-the-art neural implicit methods allow for high-quality reconstructions of simple scenes from many input views. Yet, their performance drops significantly for larger and more complex scenes and scenes captured from sparse viewpoints. This is caused primarily by the inherent ambiguity in the RGB reconstruction loss that does not provide enough constraints, in particular in less-observed and textureless areas. Motivated by recent advances in the area of monocular geometry prediction, we systematically explore the utility these cues provide for improving neural implicit surface reconstruction. We demonstrate that depth and normal cues, predicted by general-purpose monocular estimators, significantly improve reconstruction quality and optimization time. Further, we analyse and investigate multiple design choices for representing neural implicit surfaces, ranging from monolithic MLP models over single-grid to multi-resolution grid representations. We observe that geometric monocular priors improve performance both for small-scale single-object as well as large-scale multi-object scenes, independent of the choice of representation.

• The paper introduces MonoSDF, a framework that integrates monocular depth and normal cues to enhance neural implicit surface reconstruction.
• It evaluates diverse SDF representations, including dense grids, single MLPs, and multi-resolution feature grids, to boost reconstruction quality and efficiency.
• Results on DTU, Replica, and ScanNet demonstrate improved accuracy and completeness over existing baselines.

Understanding MonoSDF: Leveraging Monocular Cues for Enhanced Neural Implicit Surface Reconstruction

The paper "MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstruction" presents a methodological framework that enhances the capabilities of neural implicit surface reconstruction using monocular geometric cues. The authors introduce a system, MonoSDF, which integrates monocular depth and normal predictions into the optimization of neural implicit surfaces, thus addressing significant challenges in reconstructing complex and large-scale scenes from sparse viewpoints.

Core Contributions

The contribution of this paper is twofold: the introduction of a new framework, MonoSDF, and a comprehensive analysis of neural implicit surface representations under varying architectural configurations. By utilizing monocular depth and normal cues, the authors demonstrate a marked improvement in both reconstruction quality and computational efficiency, independent of the employed design choice, which includes dense SDF grids, single MLPs, and multi-resolution feature grids.

Methodology

Scene Representations

The paper evaluates four distinct parameterizations for representing the SDF, namely:

1. Dense SDF Grids: Offers a direct parameterization of SDF values within a grid, reducing computational overhead but lacking smoothness bias.
2. Single MLPs: Provides a global operation with an inherent smoothness bias, although less computationally efficient.
3. Single-Resolution Feature Grids with MLP Decoder: Combines local feature grids with MLP to enhance expressiveness.
4. Multi-Resolution Feature Grids (Grids): Utilizes multiple levels of feature grid resolutions that demonstrate improved results, particularly in detail preservation.

Monocular Cues Integration

Monocular cues, namely depth and normals, are computed using a pretrained network and integrated into the optimization process as supplementary constraints alongside RGB data and eikonal losses. This approach allows MonoSDF to mitigate ambiguities, particularly in textureless or minimally observed areas by the camera.

Results

The authors validate MonoSDF on datasets ranging from object-specific (DTU dataset) to large-scale scenes (Replica and ScanNet). On DTU, the approach achieves notable quantitative improvements, especially when confronting limited input views. The paper reports a shift in Chamfer distance by adopting monocular cues, signifying enhanced accuracy and completeness over established baselines such as VolSDF.

For large-scale indoor environments, experimental evaluations on ScanNet and Tanks and Temples datasets signify that MonoSDF achieves superior reconstruction quality, outperforming baselines, including recent renditions of NeuS and UNISURF.

Analysis and Implications

The paper indicates that incorporating monocular geometric cues fundamentally enhances the robustness and precision of neural implicit surface reconstruction across diverse settings. It highlights that these monocular cues serve as effective priors, mitigating the inherent underconstraints of dense or sparse RGB data configurations. Moreover, their impact on optimization speed fosters more practical applications of neural implicit reconstructions in real-time scenarios.

Although prospective developments in AI could include further refining monocular cue prediction models and exploring additional cues like occlusion boundaries or curvature, MonoSDF sets a robust benchmark for future enhancements. The cross-examination of multiple architectural designs provides insights crucial for optimizing neural implicit surfaces depending on available data and computational constraints.

In conclusion, MonoSDF introduces a significant advancement by leveraging monocular cues, evidencing a compelling approach to tackling persistent issues in neural implicit surface reconstruction. It offers a promising avenue for evolving 3D computational vision systems, particularly those navigating complex, real-world scenarios.