Surface-Centric Modeling for High-Fidelity Generalizable Neural Surface Reconstruction (2409.03634v1)

Abstract: Reconstructing the high-fidelity surface from multi-view images, especially sparse images, is a critical and practical task that has attracted widespread attention in recent years. However, existing methods are impeded by the memory constraint or the requirement of ground-truth depths and cannot recover satisfactory geometric details. To this end, we propose SuRF, a new Surface-centric framework that incorporates a new Region sparsification based on a matching Field, achieving good trade-offs between performance, efficiency and scalability. To our knowledge, this is the first unsupervised method achieving end-to-end sparsification powered by the introduced matching field, which leverages the weight distribution to efficiently locate the boundary regions containing surface. Instead of predicting an SDF value for each voxel, we present a new region sparsification approach to sparse the volume by judging whether the voxel is inside the surface region. In this way, our model can exploit higher frequency features around the surface with less memory and computational consumption. Extensive experiments on multiple benchmarks containing complex large-scale scenes show that our reconstructions exhibit high-quality details and achieve new state-of-the-art performance, i.e., 46% improvements with 80% less memory consumption. Code is available at https://github.com/prstrive/SuRF.

• The paper presents SuRF, a novel framework that leverages a matching field and region sparsification to achieve high-fidelity 3D surface reconstruction with reduced memory usage.
• The methodology employs multi-scale feature aggregation, unsupervised image warping, and focused surface sampling to robustly capture fine geometric details.
• Experimental results demonstrate a 46% performance improvement over baselines and an 80% reduction in memory usage, highlighting its potential for applications in autonomous driving, robotics, and virtual reality.

Surface-Centric Modeling for High-Fidelity Generalizable Neural Surface Reconstruction

Introduction

The paper "Surface-Centric Modeling for High-Fidelity Generalizable Neural Surface Reconstruction" by Rui Peng et al. introduces a novel framework called SuRF, aiming to address challenges in reconstructing high-fidelity surfaces from sparse multi-view images. Traditional methods in this area often suffer from high memory consumption and suboptimal geometric detail recovery due to the constraints of per-scene optimization or the necessity of extensive ground-truth depth data. This paper posits a new approach centered around surface-centric modeling, which incorporates region sparsification based on a matching field, to balance performance, efficiency, and scalability optimally.

Methodology

The SuRF framework involves several key components:

1. Cross-Scale Feature Aggregation: The model employs a multi-scale feature extraction technique via an FPN network. Multi-view features are fused using a network that weights different views' contributions, enhancing resilience to occlusions and ensuring robust geometric inference.
2. Matching Field for Surface Region Localization: Instead of conventional occupancy, density, or SDF values, the paper introduces a matching field leveraging weight distribution along rays. This innovative representation enables efficient localization of surface regions by interpolating values from a pre-computed matching volume, thus focusing computational efforts only on relevant regions of the scene. Training the matching field is achieved through an unsupervised image warping loss, which leverages multi-view consistency as a supervisory signal.
3. Region Sparsification: The sparsification process uses the identified surface regions to progressively refine the volumetric representation at multiple scales. Voxels not contributing to the surface detail are pruned based on visibility criteria across multiple views, thus reducing memory and computational overhead. The authors emphasize the robustness of this approach to occlusions, as regions must be visible from at least two perspectives to be retained.
4. Surface Sampling: Focused sampling is implemented within the identified surface regions to capture high-frequency surface details. By interpolating sparse volumes and refining the sampling within relevant regions, the model efficiently reconstructs surfaces with enhanced fidelity.

Results

Experiments conducted on benchmark datasets like DTU, BlendedMVS, Tanks and Temples, and ETH3D demonstrate SuRF's capability to achieve superior performance compared to state-of-the-art methods. The paper reports a 46% improvement over the baseline SparseNeuS and an 80% reduction in memory usage. These significant gains are attributed to the surface-centric approach, which effectively prioritizes resources on reconstructing geometrically relevant areas.

The qualitative and quantitative results underscore the model's robustness and ability to generalize across diverse and complex scenes, maintaining high-quality reconstructions even with sparse inputs. The ablation studies confirm the contributions of multi-scale architectures, region sparsification, and unsupervised warping loss in enhancing the overall reconstruction quality.

Implications and Future Directions

The implications of this research extend both practically and theoretically. Practically, SuRF is well-positioned for applications in autonomous driving, robotics, and virtual reality, where real-time high-fidelity surface reconstruction from limited viewpoints is crucial. Theoretically, this paper advances the understanding of multi-view stereo and neural rendering by demonstrating the viability of unsupervised, surface-centric sparsification.

Looking ahead, the authors suggest focusing on real-time performance improvements and expanding training datasets to cover more extensive and diverse scenes. This could include leveraging large-scale datasets like Objaverse, potentially evolving SuRF into a more scalable and versatile solution for various 3D reconstruction applications.

Conclusion

In summary, this paper presents SuRF, a groundbreaking approach to neural surface reconstruction that achieves high fidelity and efficiency through surface-centric modeling. Through innovative methodologies like matching fields and region sparsification, SuRF sets a new benchmark in the field, bridging the gap between performance and resource efficiency. This work lays the groundwork for future research aimed at enhancing real-time capabilities and handling ultra-large-scale environments.