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Found 7 papers, 1 papers with code
NIGHT -- Non-Line-of-Sight Imaging from Indirect Time of Flight Data
The acquisition of objects outside the Line-of-Sight of cameras is a very intriguing but also extremely challenging research topic.
Exploiting Multiple Priors for Neural 3D Indoor Reconstruction
In this work, we propose a novel neural implicit modeling method that leverages multiple regularization strategies to achieve better reconstructions of large indoor environments, while relying only on images.
A Low Memory Footprint Quantized Neural Network for Depth Completion of Very Sparse Time-of-Flight Depth Maps
We also achieve low memory footprint for weights and activations by means of mixed precision quantization-at-training techniques.
Lightweight Deep Learning Architecture for MPI Correction and Transient Reconstruction
Indirect Time-of-Flight cameras (iToF) are low-cost devices that provide depth images at an interactive frame rate.
Unsupervised Domain Adaptation for Mobile Semantic Segmentation based on Cycle Consistency and Feature Alignment
The supervised training of deep networks for semantic segmentation requires a huge amount of labeled real world data.
Adversarial Learning and Self-Teaching Techniques for Domain Adaptation in Semantic Segmentation
A recently proposed workaround is to train deep networks using synthetic data, but the domain shift between real world and synthetic representations limits the performance.
Unsupervised Domain Adaptation for ToF Data Denoising With Adversarial Learning
A Coarse-Fine CNN, able to exploit multi-frequency ToF data for MPI correction, is trained on synthetic data with ground truth in a supervised way.
