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

arXiv: https://arxiv.org/abs/1807.07473 · HTML: https://mlsanity.com/paper/1807.07473/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2018.

## Built on by (most cited first, 3 of 3)

- [Generalized Contrastive Optimization of Siamese Networks for Place Recognition](https://mlsanity.com/paper/2103.06638/) (2021)
- [Learning Digital Terrain Models from Point Clouds: ALS2DTM Dataset and Rasterization-based GAN](https://mlsanity.com/paper/2206.03778/) (2022)
- [Leveraging knowledge distillation for partial multi-task learning from multiple remote sensing datasets](https://mlsanity.com/paper/2405.15394/) (2024)

## Abstract (arXiv)

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.