Surface-Centric Modeling for High-Fidelity Generalizable Neural Surface Reconstruction
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 rec...
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Main Authors | , , , , , , |
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Format | Journal Article |
Language | English |
Published |
05.09.2024
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Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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DOI: | 10.48550/arxiv.2409.03634 |