Sun et al., 2023 - Google Patents

Sc-depthv3: Robust self-supervised monocular depth estimation for dynamic scenes

Sun et al., 2023

View PDF
Document ID
6723286458614088814
Author
Sun L
Bian J
Zhan H
Yin W
Reid I
Shen C
Publication year
Publication venue
IEEE Transactions on Pattern Analysis and Machine Intelligence

External Links

Snippet

Self-supervised monocular depth estimation has shown impressive results in static scenes. It relies on the multi-view consistency assumption for training networks, however, that is violated in dynamic object regions and occlusions. Consequently, existing methods show …
Continue reading at arxiv.org (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10024—Color image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10016—Video; Image sequence
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20112—Image segmentation details
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46—Extraction of features or characteristics of the image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2200/00—Indexing scheme for image data processing or generation, in general
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T15/00—3D [Three Dimensional] image rendering
    • G06T15/10—Geometric effects
    • G06T15/20—Perspective computation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—2D [Two Dimensional] image generation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30781—Information retrieval; Database structures therefor; File system structures therefor of video data
    • G06F17/30784—Information retrieval; Database structures therefor; File system structures therefor of video data using features automatically derived from the video content, e.g. descriptors, fingerprints, signatures, genre
    • G06F17/30799—Information retrieval; Database structures therefor; File system structures therefor of video data using features automatically derived from the video content, e.g. descriptors, fingerprints, signatures, genre using low-level visual features of the video content

Similar Documents

Publication Publication Date Title
Sun et al. Sc-depthv3: Robust self-supervised monocular depth estimation for dynamic scenes
Mitrokhin et al. EV-IMO: Motion segmentation dataset and learning pipeline for event cameras
Zhu et al. Unsupervised event-based learning of optical flow, depth, and egomotion
Lv et al. Learning rigidity in dynamic scenes with a moving camera for 3d motion field estimation
Hu et al. Deep depth completion from extremely sparse data: A survey
Liu Beyond pixels: exploring new representations and applications for motion analysis
Madhuanand et al. Self-supervised monocular depth estimation from oblique UAV videos
Riegler et al. Connecting the dots: Learning representations for active monocular depth estimation
Bešić et al. Dynamic object removal and spatio-temporal RGB-D inpainting via geometry-aware adversarial learning
Guo et al. Context-enhanced stereo transformer
Khan et al. An efficient encoder–decoder model for portrait depth estimation from single images trained on pixel-accurate synthetic data
Wang et al. Unsupervised learning of optical flow with non-occlusion from geometry
Xie et al. Recent advances in conventional and deep learning-based depth completion: A survey
Cho et al. Event-image fusion stereo using cross-modality feature propagation
Li et al. Deep learning based monocular depth prediction: Datasets, methods and applications
Yang et al. SAM-Net: Semantic probabilistic and attention mechanisms of dynamic objects for self-supervised depth and camera pose estimation in visual odometry applications
Lu et al. Stereo disparity optimization with depth change constraint based on a continuous video
Zhang et al. Self-supervised monocular depth estimation with self-perceptual anomaly handling
Lee et al. Self-supervised monocular depth and motion learning in dynamic scenes: Semantic prior to rescue
Xu et al. MRFTrans: Multimodal Representation Fusion Transformer for monocular 3D semantic scene completion
Xiang et al. Self-supervised monocular trained depth estimation using triplet attention and funnel activation
Lee et al. Instance-wise depth and motion learning from monocular videos
Ocal et al. Realmonodepth: Self-supervised monocular depth estimation for general scenes
Wang et al. Physical Priors Augmented Event-Based 3D Reconstruction
Chen et al. PlanarNeRF: Online Learning of Planar Primitives with Neural Radiance Fields