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Depth Prediction

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Showing 176–200 of 422 papers

TitleStatusHype
Just-in-Time Reconstruction: Inpainting Sparse Maps using Single View Depth Predictors as Priors—0
Joint Prediction of Monocular Depth and Structure using Planar and Parallax Geometry—0
JointNet: Extending Text-to-Image Diffusion for Dense Distribution Modeling—0
MiniNet: An extremely lightweight convolutional neural network for real-time unsupervised monocular depth estimation—0
Joint Depth Prediction and Semantic Segmentation with Multi-View SAM—0
Joint 3D Layout and Depth Prediction from a Single Indoor Panorama Image—0
DesNet: Decomposed Scale-Consistent Network for Unsupervised Depth Completion—0
Intrinsic Image Decomposition for Robust Self-supervised Monocular Depth Estimation on Reflective Surfaces—0
CodeVIO: Visual-Inertial Odometry with Learned Optimizable Dense Depth—0
TriDepth: Triangular Patch-based Deep Depth Prediction—0
KeyTr: Keypoint Transporter for 3D Reconstruction of Deformable Objects in Videos—0
Instance-aware multi-object self-supervision for monocular depth prediction—0
Layered Depth Refinement with Mask Guidance—0
In-situ and Non-contact Etch Depth Prediction in Plasma Etching via Machine Learning (ANN & BNN) and Digital Image Colorimetry—0
Depth Is All You Need for Monocular 3D Detection—0
Bayesian Deep Basis Fitting for Depth Completion with Uncertainty—0
MDS-Net: A Multi-scale Depth Stratification Based Monocular 3D Object Detection Algorithm—0
Meta-Optimization for Higher Model Generalizability in Single-Image Depth Prediction—0
Learning Inverse Laplacian Pyramid for Progressive Depth Completion—0
Mitigating Perspective Distortion-induced Shape Ambiguity in Image Crops—0
Improved Neural Radiance Fields Using Pseudo-depth and Fusion—0
Improved Monocular Depth Prediction Using Distance Transform Over Pre-semantic Contours with Self-supervised Neural Networks—0
Learning the Depths of Moving People by Watching Frozen People—0
Distill Knowledge from NRSfM for Weakly Supervised 3D Pose Learning—0
Hybrid Transformer Based Feature Fusion for Self-Supervised Monocular Depth Estimation—0
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