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Found 11 papers, 6 papers with code
MonoDiffusion: Self-Supervised Monocular Depth Estimation Using Diffusion Model
Because the depth ground-truth is unavailable in the training phase, we develop a pseudo ground-truth diffusion process to assist the diffusion in MonoDiffusion.
NDDepth: Normal-Distance Assisted Monocular Depth Estimation and Completion
To this end, we develop a normal-distance head that outputs pixel-level surface normal and distance.
IEBins: Iterative Elastic Bins for Monocular Depth Estimation
To alleviate the possible error accumulation during the iterative process, we utilize a novel elastic target bin to replace the original target bin, the width of which is adjusted elastically based on the depth uncertainty.
Ranked #12 on
Monocular Depth Estimation
on KITTI Eigen split
NDDepth: Normal-Distance Assisted Monocular Depth Estimation
Meanwhile, the normal and distance are regularized by a developed plane-aware consistency constraint.
Ranked #12 on
Monocular Depth Estimation
on KITTI Eigen split
StairNetV3: Depth-aware Stair Modeling using Deep Learning
Experiments on our dataset show that our method has a significant improvement over the previous best monocular vision method, with an intersection over union (IOU) increase of 3. 4 %, and the lightweight version has a fast detection speed and can meet the requirements of most real-time applications.
URCDC-Depth: Uncertainty Rectified Cross-Distillation with CutFlip for Monocular Depth Estimation
Specifically, we use the depth estimates from the Transformer branch and the CNN branch as pseudo labels to teach each other.
Ranked #12 on
Monocular Depth Estimation
on KITTI Eigen split
RGB-D-based Stair Detection using Deep Learning for Autonomous Stair Climbing
Specifically, we design a selective module, which can make the network learn the complementary relationship between the RGB map and the depth map and effectively combine the information from the RGB map and the depth map in different scenes.
SMUDLP: Self-Teaching Multi-Frame Unsupervised Endoscopic Depth Estimation with Learnable Patchmatch
Unsupervised monocular trained depth estimation models make use of adjacent frames as a supervisory signal during the training phase.
Deep Leaning-Based Ultra-Fast Stair Detection
Staircases are some of the most common building structures in urban environments.
Self-Supervised Monocular Depth and Ego-Motion Estimation in Endoscopy: Appearance Flow to the Rescue
Recently, self-supervised learning technology has been applied to calculate depth and ego-motion from monocular videos, achieving remarkable performance in autonomous driving scenarios.
Towards Comprehensive Monocular Depth Estimation: Multiple Heads Are Better Than One
In this work, we investigate into the phenomenon and propose to integrate the strengths of multiple weak depth predictor to build a comprehensive and accurate depth predictor, which is critical for many real-world applications, e. g., 3D reconstruction.
