# Deep Classification Network for Monocular Depth Estimation

Source: [Deep Classification Network for Monocular Depth Estimation](<https://www.sota2.com/research/paper/68fa66951e209131cea1120f>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:53:21.106Z

Authors: Azeez Oluwafemi, Yang Zou, B.V.K. Vijaya Kumar

Published: 2019-10-23T05:50:04.000Z

## Abstract

Monocular Depth Estimation is usually treated as a supervised and regression problem when it actually is very similar to semantic segmentation task since they both are fundamentally pixel-level classification tasks. We applied depth increments that increases with depth in discretizing depth values and then applied Deeplab v2 and the result was higher accuracy. We were able to achieve a state-of-the-art result on the KITTI dataset and outperformed existing architecture by an 8% margin.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1910.10369>)

[PDF](<https://arxiv.org/pdf/1910.10369.pdf>)