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Feihu Xu

5 accepted papers

2025

Dual-branch Graph Feature Learning for NLOS Imaging

AAAI 2025technical

The domain of non-line-of-sight (NLOS) imaging is advancing rapidly, offering the capability to reveal occluded scenes that are not directly visible. However, contemporary NLOS systems face several significant challenges: (1) The computational and storage requirements are profound due to the inheren…

Cited by 0SourcePDFScholar
2024

Toward Dynamic Non-Line-of-Sight Imaging with Mamba Enforced Temporal Consistency

NeurIPS 2024poster

Dynamic reconstruction in confocal non-line-of-sight imaging encounters great challenges since the dense raster-scanning manner limits the practical frame rate. A fewer pioneer works reconstruct high-resolution volumes from the under-scanning transient measurements but overlook temporal consistency…

2023

Deep Non-line-of-sight Imaging from Under-scanning Measurements

NeurIPS 2023poster

Active confocal non-line-of-sight (NLOS) imaging has successfully enabled seeing around corners relying on high-quality transient measurements. However, acquiring spatial-dense transient measurement is time-consuming, raising the question of how to reconstruct satisfactory results from under-scannin…

2023

NLOST: Non-Line-of-Sight Imaging With Transformer

CVPR 2023poster

Time-resolved non-line-of-sight (NLOS) imaging is based on the multi-bounce indirect reflections from the hidden objects for 3D sensing. Reconstruction from NLOS measurements remains challenging especially for complicated scenes. To boost the performance, we present NLOST, the first transformer-base…

Cited by 29SourcePDFScholar
2020

Photon-Efficient 3D Imaging with A Non-Local Neural Network

ECCV 2020poster

Photon-efficient imaging has enabled a number of applications relying on single-photon sensors that can capture a 3D image with as few as one photon per pixel. In practice, however, measurements of low photon counts are often mixed with heavy background noise, which poses a great challenge for exist…