SCADE: NeRFs from Space Carving with Ambiguity-Aware Depth Estimates (2303.13582v1)

Abstract: Neural radiance fields (NeRFs) have enabled high fidelity 3D reconstruction from multiple 2D input views. However, a well-known drawback of NeRFs is the less-than-ideal performance under a small number of views, due to insufficient constraints enforced by volumetric rendering. To address this issue, we introduce SCADE, a novel technique that improves NeRF reconstruction quality on sparse, unconstrained input views for in-the-wild indoor scenes. To constrain NeRF reconstruction, we leverage geometric priors in the form of per-view depth estimates produced with state-of-the-art monocular depth estimation models, which can generalize across scenes. A key challenge is that monocular depth estimation is an ill-posed problem, with inherent ambiguities. To handle this issue, we propose a new method that learns to predict, for each view, a continuous, multimodal distribution of depth estimates using conditional Implicit Maximum Likelihood Estimation (cIMLE). In order to disambiguate exploiting multiple views, we introduce an original space carving loss that guides the NeRF representation to fuse multiple hypothesized depth maps from each view and distill from them a common geometry that is consistent with all views. Experiments show that our approach enables higher fidelity novel view synthesis from sparse views. Our project page can be found at https://scade-spacecarving-nerfs.github.io .

• The paper introduces SCADE, a novel method that uses ambiguity-aware monocular depth estimates to enhance NeRF-based 3D reconstructions from sparse views.
• It employs a multilevel synthesis via cIMLE to model multimodal depth distributions and integrates a space carving loss for improved depth disambiguation.
• Extensive experiments on datasets like ScanNet and Tanks and Temples demonstrate superior photometric accuracy and detailed object retrieval in challenging settings.

Expert Evaluation of "SCADE: NeRFs from Space Carving with Ambiguity-Aware Depth Estimates"

The paper introduces SCADE, a novel methodology aimed at improving Neural Radiance Fields (NeRFs) for 3D reconstruction from sparse and unconstrained views, a challenge that previous approaches have struggled with. By leveraging monocular depth priors and addressing inherent ambiguities in depth estimation, SCADE fills a critical gap in the flexibility and adaptability of NeRF technologies in less constrained real-world settings.

The primary innovation lies in the method's ability to tackle the inherent multimodality in monocular depth distributions and the ray termination distances induced by NeRFs. This is done by employing a multilevel synthesis approach using a conditional Implicit Maximum Likelihood Estimation (cIMLE) that allows depth predictions to reflect diverse hypotheses based on viewing variation. The method consciously models probabilistic distributions over depth and integrates this with a novel space carving loss, thereby effectively decoding these depths into consistent 3D reconstructions.

The specific contributions of this paper can be summarized as follows:

• Multimodal Depth Representation: SCADE advances the NeRF framework by encoding a distribution of possible depths for each view through ambiguity-aware prior estimates. This provides a structurally richer inference model accommodating for non-opaque surfaces and other complicating factors.
• Space Carving Loss: This is a notable contribution in how it uses sample-based techniques on distributions, enabling better depth disambiguation through comparing depth distributions from various views. Unlike traditional 2D methods, this approach offers 3D supervision, enabling it to avoid visual artifacts often introduced by depth ambiguities.
• Extensive Empirical Validation: The method is rigorously evaluated on datasets like ScanNet and Tanks and Temples, as well as custom in-the-wild datasets. SCADE outperforms baseline models, showing superior photometric accuracy and object detail retrieval in sparse view settings.

From a technical standpoint, SCADE pushes the boundaries of what can be achieved with NeRF systems in practical scenarios with limited data. Practically, it demonstrates applicability to in-the-wild datasets, showcasing robustness against wide domain data and suggesting potential integration into real-world applications, such as augmented reality and dynamic scene rendering.

However, the approach does heavily rely on the quality and domain fit of monocular depth priors, and performance could degrade with high levels of domain mismatch. Future works might explore adaptive prior calibration mechanisms or the integration of other scene recognition techniques to further mitigate such issues.

In conclusion, SCADE represents a significant step forward in making NeRF-based 3D reconstructions more feasible outside controlled environments, broadening their utility and accessibility. This methodology also opens avenues for tackling depth ambiguity in other areas of AI and computer vision, potentially inspiring further research into depth estimation and fusion in dynamically constrained settings.