Neural Mesh Fusion: UNSUPERVISED 3D PLANAR SURFACE UNDERSTANDING (2024)
Neural Mesh Fusion (NMF) is a method for joint optimization of polygon meshes, estimated from multi-view image observations and unsupervised 3D planar-surface parsing of the scene. In contrast to implicit neural representations, NMF directly learns to deform surface triangle mesh and generate an embedding for unsupervised 3D planar segmentation through gradient-based optimization directly on the surface mesh. The conducted experiments show that NMF obtains competitive results compared to state-of-the-art multi-view planar reconstruction, while not requiring any ground-truth 3D or planar supervision.
VaLID: Variable-Length Input Diffusion for Novel View Synthesis (2023)
VaLID is a deep generative AI model. It performs novel-view image synthesis by processing either a single input image or variable-length multi-view images. This highly adaptive property of VaLID enables various applications in 3D reconstruction and novel view synthesis.
Deep Learning Frameworks for Weakly-supervised Indoor Wi-Fi Positioning (2020-2021)
Deep learning solutions for the large-scale indoor localization of people and robot agents via Wi-Fi signal with accessing only to a few room/zone level annotations.
Corresponding manuscripts:
Farhad G. Zanjani, Ilia Karmanov, Hanno Achermann, Daniel Dijkman, Simone Merlin, Max Welling, Fatih Porikli.
NeurIPS, 2021.
Ilia Karmanov, Farhad G. Zanjani, Simone Merlin, Ishaque Kadampot, Daniel Dijkman
IEEE Globecom, 2021.
Farhad G. Zanjani, Ilia Karmanov et al., NeurIPS Competitions and Demonstrations Track, 2021.