Aug 27, 2018 · A sparsity-invariant multi-scale encoder-decoder network (HMS-Net) for handling sparse inputs and sparse feature maps is also proposed.

It generates sparse depth maps, which might be insufficient for many real-world applica- tions. Depth completion algorithms could estimate dense depth maps from ...

A sparsity-invariant multi-scale encoder-decoder network (HMS-Net) for handling sparse inputs and sparse feature maps is proposed, and a model withoutRGB ...

Huang et al. [11] proposed a sparsity-invariant multi-scale encoder-decoder network (HMS-Net) for depth completion to handle sparse inputs and feature maps.

However, depth maps obtained by LIDAR are generally sparse because of its hardware limitation. The task of depth completion attracts increasing attention, which ...

A sparsity-invariant multi-scale encoder-decoder network (HMS-Net) for handling sparse inputs and sparse feature maps is proposed, and a model withoutRGB ...

Duration: 1:44
Posted: Jun 29, 2020

HMS-Net: Hierarchical Multi-scale Sparsity-invariant Network for Sparse Depth Completion ... Dense depth cues are important and have wide applications in various ...

Nov 11, 2019 · HMS-Net Hierarchical Multi-scale Sparsity-invariant Network for Sparse Depth Completion. (ArXiv 18) [paper]; Propagating Confidences through ...

The current state-of-the-art on KITTI Depth Completion is SemAttNet. See a full comparison of 16 papers with code.