Geo-Neus: Geometry-Consistent Neural Implicit Surfaces Learning for Multi-view Reconstruction
By Qiancheng Fu, Qingshan Xu, Yew-Soon Ong, Wenbing Tao. Neural Information Processing Systems, 2022
319 citations recorded by the source corpus.
Recently, neural implicit surfaces learning by volume rendering has become popular for multi-view reconstruction. However, one key challenge remains: existing approaches lack explicit multi-view geometry constraints, hence usually fail to generate geometry consistent surface reconstruction. To address this challenge, we propose geometry-consistent neural implicit surfaces learning for multi-view reconstruction. We theoretically analyze that there exists a gap between the volume rendering integral and point-based signed distance function (SDF) modeling. To bridge this gap, we directly locate the zero-level set of SDF networks and explicitly perform multi-view geometry optimization by leveraging the sparse geometry from structure from motion (SFM) and photometric consistency in multi-view stereo. This makes our SDF optimization unbiased and allows the multi-view geometry constraints to focus on the true surface optimization. Extensive experiments show that our proposed method achieves high-quality surface reconstruction in both complex thin structures and large smooth regions, thus outperforming the state-of-the-arts by a large margin.
Subfields: Computational Mechanics, Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/ddec2b4399d7568fa53525d04df170418b770c0d
Citations in the corpus
- DUSt3R: Geometric 3D Vision Made Easy (2023, unknown): “Considering recent advances, this rather complex chain is of course a viable solution in some settings [31, 71, 77, 143, 146, 148, 163], yet we argue it is quite unsatisfactory: each sub-problem is not solved perfectly and adds noise to the next step, increasing the complexity and the engineering…”
- 2D Gaussian Splatting for Geometrically Accurate Radiance Fields (2024, unknown): “We also extend the original 3DGS to render depth and employ the same technique for surface reconstruction for a fair comparison.”
- PGSR: Planar-Based Gaussian Splatting for Efficient and High-Fidelity Surface Reconstruction (2024, unknown): “We compared our method, PGSR, with current state-of-the-art neural surface reconstruction methods including NeuS [19], Geo-NeuS [60], VolSDF [51], and NeuralAngelo [18].”
- VGGT: Visual Geometry Grounded Transformer (2025, unknown): “…from multiple overlapping images, typically assuming known camera parameters, which are often estimated with SfM. MVS methods can be divided into three categories: traditional handcrafted [26, 27, 64, 88], global optimization [25, 50, 91, 100], and learning-based meth-ods [30, 49, 55, 99, 106].”
- NeRF: Neural Radiance Field in 3D Vision, A Comprehensive Review (2022, unknown): “The model achieved good results on the CelebA dataset [115], the CARLA dataset [116], and the ShapeNet subclasses from SRN [119], but is outperformed by its backbone π -GAN for conditional image synthesis.”