Indoor Monocular Depth Estimation

5 papers with code • 1 benchmarks • 4 datasets

This task has no description! Would you like to contribute one?

Most implemented papers

SC-DepthV3: Robust Self-supervised Monocular Depth Estimation for Dynamic Scenes

JiawangBian/sc_depth_pl 7 Nov 2022

Self-supervised monocular depth estimation has shown impressive results in static scenes.

AI Playground: Unreal Engine-based Data Ablation Tool for Deep Learning

MMehdiMousavi/AIP 13 Jul 2020

With AIP, it is trivial to capture the same image under different conditions (e. g., fidelity, lighting, etc.)

DepthLab: Real-Time 3D Interaction With Depth Maps for Mobile Augmented Reality

googlesamples/arcore-depth-lab 22 Oct 2020

Slow adoption of depth information in the UX layer may be due to the complexity of processing depth data to simply render a mesh or detect interaction based on changes in the depth map.

Learning to Recover 3D Scene Shape from a Single Image

aim-uofa/AdelaiDepth CVPR 2021

Despite significant progress in monocular depth estimation in the wild, recent state-of-the-art methods cannot be used to recover accurate 3D scene shape due to an unknown depth shift induced by shift-invariant reconstruction losses used in mixed-data depth prediction training, and possible unknown camera focal length.

InSpaceType: Reconsider Space Type in Indoor Monocular Depth Estimation

DepthComputation/InSpaceType_Benchmark 24 Sep 2023

To facilitate our investigation for robustness and address limitations of previous works, we collect InSpaceType, a high-quality and high-resolution RGBD dataset for general indoor environments.