Indoor Monocular Depth Estimation
5 papers with code • 1 benchmarks • 4 datasets
DIODE
HUMAN4D
InSpaceType
ISOD
Most implemented papers
SC-DepthV3: Robust Self-supervised Monocular Depth Estimation for Dynamic Scenes
Self-supervised monocular depth estimation has shown impressive results in static scenes.
AI Playground: Unreal Engine-based Data Ablation Tool for Deep Learning
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
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
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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
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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.
