NYU Depth V2

The NYU-Depth V2 data set is comprised of video sequences from a variety of indoor scenes as recorded by both the RGB and Depth cameras from the Microsoft Kinect.

Papers using NYU Depth V2 (9)

DPNeXt: A Lightweight Multi-Scale Feature Fusion Framework for Efficient ViT-Based Multi-Task Dense Prediction2026RBF Weighted Hyper-Involution for RGB-D Object Detection2023 · 1 citesFutureDepth: Learning to Predict the Future Improves Video Depth Estimation2024 · 5 citesWEDepth: Efficient Adaptation of World Knowledge for Monocular Depth Estimation2025DiffPixelFormer: Differential Pixel-Aware Transformer for RGB-D Indoor Scene Segmentation2025Enhancing Transformer-Based Vision Models: Addressing Feature Map Anomalies Through Novel Optimization Strategies2025Monocular Semantic Scene Completion via Masked Recurrent Networks2025Single image depth estimation by dilated deep residual convolutional neural network and soft-weight-sum inference2017 · 19 citesJoint Semantic Segmentation and Depth Estimation with Deep Convolutional Networks2016