# CReaM: Condensed Real-time Models for Depth Prediction using Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1807.08931 · HTML: https://mlsanity.com/paper/1807.08931/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2018.

## Built on by (most cited first, 4 of 4)

- [Single Image Depth Prediction with Wavelet Decomposition](https://mlsanity.com/paper/2106.02022/) (2021)
- [METER: a mobile vision transformer architecture for monocular depth estimation](https://mlsanity.com/paper/2403.08368/) (2024)
- [Lightweight Monocular Depth Estimation with an Edge Guided Network](https://mlsanity.com/paper/2209.14829/) (2022)
- [BoRe-Depth: Self-supervised Monocular Depth Estimation with Boundary Refinement for Embedded Systems](https://mlsanity.com/paper/2511.04388/) (2025)

## Abstract (arXiv)

Since the resurgence of CNNs the robotic vision community has developed a range of algorithms that perform classification, semantic segmentation and structure prediction (depths, normals, surface curvature) using neural networks. While some of these models achieve state-of-the art results and super human level performance, deploying these models in a time critical robotic environment remains an ongoing challenge. Real-time frameworks are of paramount importance to build a robotic society where humans and robots integrate seamlessly. To this end, we present a novel real-time structure prediction framework that predicts depth at 30fps on an NVIDIA-TX2. At the time of writing, this is the first piece of work to showcase such a capability on a mobile platform. We also demonstrate with extensive experiments that neural networks with very large model capacities can be leveraged in order to train accurate condensed model architectures in a "from teacher to student" style knowledge transfer.