@misc{10481/107732,
year = {2025},
month = {10},
url = {https://hdl.handle.net/10481/107732},
abstract = {In recent years, SLAM, visual odometry, and structure-from-motion approaches have
widely addressed the problems of 3D reconstruction and ego-motion estimation. Of the
many input modalities that can be used to solve these ill-posed problems, the pure visual
alternative using a single monocular RGB camera has attracted the attention of multiple
researchers due to its low cost and widespread availability in handheld devices. One
of the best proposals currently available is the Direct Sparse Odometry (DSO) system,
which has demonstrated the ability to accurately recover trajectories and depth maps using
monocular sequences as the only source of information. Given the impressive advances in
single-image depth estimation using neural networks, this work proposes an extension of
the DSO system, named DeepDSO. DeepDSO effectively integrates the state-of-the-art NeW
CRF neural network as a depth estimation module, providing depth prior information
for each candidate point. This reduces the point search interval over the epipolar line.
This integration improves the DSO algorithm’s depth point initialization and allows each
proposed point to converge faster to its true depth. Experimentation carried out in the TUMMono dataset demonstrated that adding the neural network depth estimation module to
the DSO pipeline significantly reduced rotation, translation, scale, start-segment alignment,
end-segment alignment, and RMSE errors.},
organization = {CropID Project (Ref. AS No. 25)},
publisher = {MDPI},
keywords = {CNN direct sparse odometry},
keywords = {Monocular visual odometry},
keywords = {Monocular 3D reconstruction},
title = {Deep-DSO: Improving Mapping of Direct Sparse Odometry Using CNN-Based Single-Image Depth Estimation},
doi = {10.3390/math13203330},
author = {Herrera-Granda, Erick P. and Torres-Cantero, Juan C. and Herrera-Granda, Israel D. and Lucio-Naranjo, José F},
}