# Three for one and one for three: Flow, Segmentation, and Surface Normals

Source: [Three for one and one for three: Flow, Segmentation, and Surface Normals](<https://www.sota2.com/research/paper/68fa64981e209131cea055c9>)

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

Data updated: 2025-10-25T21:07:03.168Z

Authors: Hoang-An Le, Anil S. Baslamisli, Thomas Mensink, Theo Gevers

Published: 2018-07-19T14:54:21.000Z

## Abstract

Optical flow, semantic segmentation, and surface normals represent different information modalities, yet together they bring better cues for scene understanding problems. In this paper, we study the influence between the three modalities: how one impacts on the others and their efficiency in combination. We employ a modular approach using a convolutional refinement network which is trained supervised but isolated from RGB images to enforce joint modality features. To assist the training process, we create a large-scale synthetic outdoor dataset that supports dense annotation of semantic segmentation, optical flow, and surface normals. The experimental results show positive influence among the three modalities, especially for objects' boundaries, region consistency, and scene structures.

## 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/1807.07473>)

[PDF](<https://arxiv.org/pdf/1807.07473.pdf>)