Gradient-SDF: A Semi-Implicit Surface Representation for 3D Reconstruction
Sommer C, Sang L, Schubert D, Cremers D (2022)
Publication Type: Conference contribution
Publication year: 2022
Journal
Publisher: IEEE Computer Society
Book Volume: 2022-June
Pages Range: 6270-6279
Conference Proceedings Title: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Event location: New Orleans, LA, USA
ISBN: 9781665469463
DOI: 10.1109/CVPR52688.2022.00618
Abstract
We present Gradient-SDF, a novel representation for 3D geometry that combines the advantages of implict and explicit representations. By storing at every voxel both the signed distance field as well as its gradient vector field, we enhance the capability of implicit representations with approaches originally formulated for explicit surfaces. As concrete examples, we show that (1) the Gradient-SDF allows us to perform direct SDF tracking from depth images, using efficient storage schemes like hash maps, and that (2) the Gradient-SDF representation enables us to perform photometric bundle adjustment directly in a voxel representation (without transforming into a point cloud or mesh), naturally a fully implicit optimization of geometry and camera poses and easy geometry upsampling. Experimental results confirm that this leads to significantly sharper reconstructions. Since the overall SDF voxel structure is still respected, the proposed Gradient-SDF is equally suited for (GPU) parallelization as related approaches.
Involved external institutions
Germany (DE)How to cite
APA:
Sommer, C., Sang, L., Schubert, D., & Cremers, D. (2022). Gradient-SDF: A Semi-Implicit Surface Representation for 3D Reconstruction. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 6270-6279). New Orleans, LA, USA: IEEE Computer Society.
MLA:
Sommer, Christiane, et al. "Gradient-SDF: A Semi-Implicit Surface Representation for 3D Reconstruction." Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA IEEE Computer Society, 2022. 6270-6279.
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