DFuseNet: Deep Fusion of RGB and Sparse Depth Information for Image Guided Dense Depth Completion
This addresses depth completion for robotics or autonomous systems, but it is incremental as it builds on existing fusion methods with a novel architectural tweak.
The paper tackles the problem of dense depth completion by fusing RGB images with sparse depth measurements, proposing a CNN architecture that separately extracts contextual cues from both modalities before fusing them. The approach achieves results comparable to state-of-the-art methods and generalizes well across multiple datasets.
In this paper we propose a convolutional neural network that is designed to upsample a series of sparse range measurements based on the contextual cues gleaned from a high resolution intensity image. Our approach draws inspiration from related work on super-resolution and in-painting. We propose a novel architecture that seeks to pull contextual cues separately from the intensity image and the depth features and then fuse them later in the network. We argue that this approach effectively exploits the relationship between the two modalities and produces accurate results while respecting salient image structures. We present experimental results to demonstrate that our approach is comparable with state of the art methods and generalizes well across multiple datasets.