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Depth Prediction

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Showing 301–350 of 422 papers

TitleStatusHype
Self-supervised Learning for Dense Depth Estimation in Monocular Endoscopy—0
Self-supervised Learning for Single View Depth and Surface Normal Estimation—0
Self-Supervised Learning of Depth and Camera Motion from 360° Videos—0
Self-supervised Pretraining and Finetuning for Monocular Depth and Visual Odometry—0
Self-Supervised Siamese Learning on Stereo Image Pairs for Depth Estimation in Robotic Surgery—0
Semantic Object Prediction and Spatial Sound Super-Resolution with Binaural Sounds—0
Semantics-Driven Unsupervised Learning for Monocular Depth and Ego-Motion Estimation—0
SimpleMapping: Real-Time Visual-Inertial Dense Mapping with Deep Multi-View Stereo—0
Simplify Implant Depth Prediction as Video Grounding: A Texture Perceive Implant Depth Prediction Network—0
Simultaneous Localization And Mapping with depth Prediction using Capsule Networks for UAVs—0
Single Image Depth Prediction Made Better: A Multivariate Gaussian Take—0
SLAM Endoscopy enhanced by adversarial depth prediction—0
Sparse2Dense: From direct sparse odometry to dense 3D reconstruction—0
Sparse Depth Completion with Semantic Mesh Deformation Optimization—0
Speed estimation evaluation on the KITTI benchmark based on motion and monocular depth information—0
Splatter a Video: Video Gaussian Representation for Versatile Processing—0
SSAP: A Shape-Sensitive Adversarial Patch for Comprehensive Disruption of Monocular Depth Estimation in Autonomous Navigation Applications—0
STDepthFormer: Predicting Spatio-temporal Depth from Video with a Self-supervised Transformer Model—0
Structured Depth Prediction in Challenging Monocular Video Sequences—0
Structure Flow-Guided Network for Real Depth Super-Resolution—0
STS: Surround-view Temporal Stereo for Multi-view 3D Detection—0
SuperDepth: Self-Supervised, Super-Resolved Monocular Depth Estimation—0
Synergistic saliency and depth prediction for RGB-D saliency detection—0
TDC-less Direct Time-of-Flight Imaging Using Spiking Neural Networks—0
Temporally Consistent Depth Prediction with Flow-Guided Memory Units—0
ToSA: Token Selective Attention for Efficient Vision Transformers—0
Toward Accurate Camera-based 3D Object Detection via Cascade Depth Estimation and Calibration—0
Monocular Outdoor Semantic Mapping with a Multi-task Network—0
Towards Domain Generalization for Multi-view 3D Object Detection in Bird-Eye-View—0
Towards Multimodal Depth Estimation from Light Fields—0
Towards Unified Depth and Semantic Prediction From a Single Image—0
Tracktention: Leveraging Point Tracking to Attend Videos Faster and Better—0
UAMD-Net: A Unified Adaptive Multimodal Neural Network for Dense Depth Completion—0
UniBEVFusion: Unified Radar-Vision BEVFusion for 3D Object Detection—0
UniUIR: Considering Underwater Image Restoration as An All-in-One Learner—0
Unsupervised Monocular Depth Prediction for Indoor Continuous Video Streams—0
Vision and Learning for Deliberative Monocular Cluttered Flight—0
Visual Localization Using Semantic Segmentation and Depth Prediction—0
Visual Relationship Prediction via Label Clustering and Incorporation of Depth Information—0
Voyager: Long-Range and World-Consistent Video Diffusion for Explorable 3D Scene Generation—0
Vyākarana: A Colorless Green Benchmark for Syntactic Evaluation in Indic Languages—0
Wavelet Feature Maps Compression for Low Bandwidth Convolutional Neural Networks—0
Web Stereo Video Supervision for Depth Prediction from Dynamic Scenes—0
WildFusion: Learning 3D-Aware Latent Diffusion Models in View Space—0
Wild ToFu: Improving Range and Quality of Indirect Time-of-Flight Depth with RGB Fusion in Challenging Environments—0
Win-Win: Training High-Resolution Vision Transformers from Two Windows—0
SharpNet: Fast and Accurate Recovery of Occluding Contours in Monocular Depth EstimationCode0
Veritatem Dies Aperit - Temporally Consistent Depth Prediction Enabled by a Multi-Task Geometric and Semantic Scene Understanding ApproachCode0
Sparse and noisy LiDAR completion with RGB guidance and uncertaintyCode0
Sparse and noisy LiDAR completion with RGB guidance anduncertaintyCode0
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