3D Reconstruction
Learning-based 3D reconstruction from multi-view images, combining deep learning and geometric reasoning to recover detailed scene structure.
3D ReconstructionMulti-View StereoPoint Cloud UpsamplingDepth Estimation
Projects
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Publications
Blending 3D geometry and machine learning for multi-view stereopsis
2025
Vibhas Vats, Md. Alimoor Reza, David Crandall, Soon-heung Jung • Neurocomputing
GC-MVSNet++ improves the reconstruction quality of 3D scenes by introducing a novel cost-regularization network based on the principles of dense-connnections. It produces competitive results to state-of-the-art MVSFormer++ paper without using Transformers.
3D ReconstructionMulti-View Stereo
Geometric Constraints in Deep Learning Frameworks: A Survey
2025
Vibhas Kumar Vats, David Crandall • ACM Computing Surveys
This survey examines how geometry can be integrated into deep learning frameworks for depth estimation and related computer vision problems. It presents a taxonomy of common geometry-based constraints, compares existing methods, and discusses future research opportunities.
3D ReconstructionDepth EstimationMulti-View StereoSurvey
HVPUNet: Hybrid-Voxel Point-cloud Upsampling Network
2025
Huyung Ha, Vibhas Kumar Vats, Soon-heung Jung, Alimoor Reza, David Crandall • ICCV
This paper presents an efficient method for point-cloud upsampling from sparse or incomplete 3D data. We propose Hybrid Voxels, which combine the efficiency of voxel-based processing with the precision of point-based representations, and build HVPUNet on top of this design. The resulting framework restores missing geometry, refines surface detail, and achieves strong accuracy with lower computational cost.
3D ReconstructionSuper ResolutionPoint Cloud Upsampling
Deep learning-based 3D reconstruction from multiple images: A survey
2024
Chuhua Wang, Md. Alimoor Reza, Vibhas Kumar Vats, Yingnan Ju, Nikhil Thakurdesai, Yuchen Wang, David Crandall, Soon-heung Jung, Jeongil Seo • Neurocomputing
This survey presents an overview of deep learning approaches for 3D reconstruction. It groups existing work into major problem settings and compares methods based on their architecture, outputs, datasets, and quantitative results. The paper also highlights current challenges and promising directions for future research.
Survey3D ReconstructionMulti-View Stereo
GC-MVSNet: Multi-View, Multi-Scale, Geometrically-Consistent Multi-View Stereo
2024
Vibhas Vats, Sripad Joshi, David Crandall, Md. Alimoor Reza, Soon-heung Jung • WACV
GC-MVSnet explicitly models geomtric consistency constraints across different views to acceralte optimization and geometric understanding of learning-based models. We produce state-of-the-art results on DTU, BlendedMVS, and Tanks & Temples datasets.
3D ReconstructionMulti-View Stereo