GeoTransfer : Generalizable Few-Shot Multi-View Reconstruction via Transfer Learning (2408.14724v2)

Abstract: This paper presents a novel approach for sparse 3D reconstruction by leveraging the expressive power of Neural Radiance Fields (NeRFs) and fast transfer of their features to learn accurate occupancy fields. Existing 3D reconstruction methods from sparse inputs still struggle with capturing intricate geometric details and can suffer from limitations in handling occluded regions. On the other hand, NeRFs excel in modeling complex scenes but do not offer means to extract meaningful geometry. Our proposed method offers the best of both worlds by transferring the information encoded in NeRF features to derive an accurate occupancy field representation. We utilize a pre-trained, generalizable state-of-the-art NeRF network to capture detailed scene radiance information, and rapidly transfer this knowledge to train a generalizable implicit occupancy network. This process helps in leveraging the knowledge of the scene geometry encoded in the generalizable NeRF prior and refining it to learn occupancy fields, facilitating a more precise generalizable representation of 3D space. The transfer learning approach leads to a dramatic reduction in training time, by orders of magnitude (i.e. from several days to 3.5 hrs), obviating the need to train generalizable sparse surface reconstruction methods from scratch. Additionally, we introduce a novel loss on volumetric rendering weights that helps in the learning of accurate occupancy fields, along with a normal loss that helps in global smoothing of the occupancy fields. We evaluate our approach on the DTU dataset and demonstrate state-of-the-art performance in terms of reconstruction accuracy, especially in challenging scenarios with sparse input data and occluded regions. We furthermore demonstrate the generalization capabilities of our method by showing qualitative results on the Blended MVS dataset without any retraining.

• The paper introduces a transfer learning strategy that adapts GeoNeRF features to learn accurate occupancy fields, reducing training from days to hours.
• It presents novel volumetric rendering weight and normal loss functions that enhance geometric detail and mitigate occlusion challenges.
• GeoTransfer achieves state-of-the-art performance on DTU and BlendedMVS datasets, setting new benchmarks for sparse-view 3D reconstruction.

GeoTransfer: Generalizable Few-Shot Multi-View Reconstruction via Transfer Learning

The paper introduces "GeoTransfer," a robust approach aimed at enhancing the efficacy and efficiency of 3D reconstruction from sparse input images. The proposed methodology leverages the advancement in Neural Radiance Fields (NeRFs) combined with transfer learning techniques to swiftly adapt pre-trained features for precise 3D surface reconstructions.

Context and Contributions

The field of 3D reconstruction has seen significant advancements owing to developments in neural implicit representations and Multi-view Stereo (MVS) techniques. However, these methods often grapple with substantial computational requirements, limitations in dealing with occlusions, and challenges in detailed geometric capture. Sparse 3D reconstruction from fewer views introduces additional complexity, as traditional methods struggle with the cross-scene generalization and require fine-tuning for new scenes.

The authors position GeoTransfer within this context, proposing a method that bridges the gap between the high-fidelity scene modeling capabilities of NeRFs and the need for efficient and accurate 3D reconstruction from limited input views.

The contributions of the paper are as follows:

1. Transfer Learning Strategy: By adapting a pre-trained generalizable NeRF (GeoNeRF) to learn occupancy fields, GeoTransfer circumvents the computational overhead typically associated with training from scratch. This technique effectively reduces the training time to approximately 3.5 hours from several days.
2. Loss Functions for Occupancy Learning: The introduction of a novel volumetric rendering weight loss and a normal-based smoothing loss, which facilitate accurate occupancy field learning, are critical innovations in GeoTransfer.
3. State-of-the-Art Performance: GeoTransfer achieves superior reconstruction accuracy on the DTU dataset, especially under sparse input conditions. Additionally, it exhibits strong generalization capabilities, demonstrating qualitative robustness on the BlendedMVS dataset without retraining.

Methodology

Leveraging GeoNeRF Features

The core of GeoTransfer lies in its ability to utilize a pre-trained GeoNeRF model. GeoNeRF excels in constructing detailed radiance maps for novel-view synthesis but lacks mechanisms for direct geometric extraction. GeoTransfer capitalizes on the feature representations learned by GeoNeRF and adapts them to derive implicit 3D occupancy fields.

The method involves transferring the features of the pre-trained GeoNeRF network to an occupancy network, thereby enabling the transformation of sampling-dependent opacity information into sampling-independent occupancy fields. This transformation is pivotal as it provides a spatially-consistent geometry representation essential for accurate surface reconstruction.

Novel Loss Functions

The paper introduces two novel loss functions:

• Volumetric Rendering Weight Loss: This loss ensures the learned occupancy fields adhere to properties consistent with a theoretical occupancy function, i.e., the occupancy peaks at the surface intersection along a ray.
• Normal Loss: By enforcing smooth normal transitions within the occupancy field, this loss helps to reduce artifacts and noise, resulting in more visually coherent reconstructions.

These loss functions are designed to refine the feature space, allowing the transfer learning process to yield high-fidelity occupancy fields rapidly.

Experimental Results

DTU Dataset Evaluation

GeoTransfer's performance is rigorously evaluated on the DTU dataset, with results indicating state-of-the-art accuracy in 3D reconstruction from sparse views:

• GeoTransfer outperforms existing methods such as SparseNeuS, VolRecon, and ReTR by significant margins.
• Numerical results underscore the capability of GeoTransfer in preserving fine geometric details and delivering accurate reconstructions even with occluded regions and sparse inputs.

The presented comparison in Chamfer distances highlights GeoTransfer's superiority, showcasing improvements of up to 30% over benchmark methods.

Generalization on BlendedMVS

The generalization prowess of GeoTransfer is further validated on the BlendedMVS dataset, where qualitative results demonstrate the method's robustness in varied and challenging scenarios without any additional fine-tuning:

• GeoTransfer consistently outperforms existing state-of-the-art methods in capturing intricate details and producing high-fidelity surface reconstructions.

Novel View Synthesis

GeoTransfer not only excels in 3D reconstruction but also retains strong performance in the task of novel-view synthesis:

• It achieves near-parity with GeoNeRF, the baseline model for novel view synthesis, thereby demonstrating the dual capability of accurate geometrical reconstruction and high-quality novel-view synthesis.

Implications and Future Directions

GeoTransfer's success suggests several practical and theoretical implications:

• The ability to perform rapid and accurate 3D reconstructions from sparse views has direct applications in fields like robotic vision, VR/AR, and cultural heritage preservation.
• The efficiency gained through the transfer learning strategy could spur further research into more targeted and specialized 3D reconstruction algorithms, potentially exploring architectures beyond NeRF-inspired models.

Future developments could focus on extending the methodology to handle dynamic scenes or incorporating temporal coherence for video-based 3D reconstructions. Furthermore, integrating more sophisticated feature learning mechanisms could enhance the generalization capabilities even further, paving the way for more robust and adaptive 3D reconstruction systems.

In conclusion, GeoTransfer embodies a significant advancement in the quest for efficient and precise 3D reconstruction from sparse inputs. Its innovative use of transfer learning combined with sophisticated loss functions sets a high bar for future research in the domain.