# CReaM: Condensed Real-time Models for Depth Prediction using Convolutional Neural Networks Source: [CReaM: Condensed Real-time Models for Depth Prediction using Convolutional Neural Networks]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:07:03.168Z Authors: Andrew Spek, Thanuja Dharmasiri, Tom Drummond Published: 2018-07-24T07:16:54.000Z ## Abstract 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. ## 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]() [PDF]()