Carte de disparité

Résumé

La carte de disparité correspondant à une scène est une qui contient uniquement l’information sur les correspondances des points entre deux vues d'une même scène prises à deux endroits différents.

Stéréovision
Ombroscopie
Scanner 3D

Catégorie:Vision artificielle Catégorie:Stéréoscopie

Source officielle

https://fr.wikipedia.org/wiki/Carte_de_disparit%C3%A9

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Publications associées (38)

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Global Auto-regressive Depth Recovery via Iterative Non-local Filtering

Pascal Frossard, Jingyu Yang, Xinchen Ye

Existing depth sensing techniques have many shortcomings in terms of resolution, completeness, and accuracy. The performance of 3-D broadcasting systems is therefore limited by the challenges of capturing high-resolution depth data. In this paper, we present a novel framework for obtaining high-quality depth images and multi-view depth videos from simple acquisition systems. We first propose a single depth image recovery algorithm based on auto-regressive (AR) correlations. A fixed-point iteration algorithm under the global AR modeling is derived to efficiently solve the large-scale quadratic programming. Each iteration is equivalent to a nonlocal filtering process with a residue feedback. Then, we extend our framework to an AR-based multi-view depth video recovery framework, where each depth map is recovered from low-quality measurements with the help of the corresponding color image, depth maps from neighboring views, and depth maps of temporally adjacent frames. AR coefficients on nonlocal spatiotemporal neighborhoods in the algorithm are designed to improve the recovery performance. We further discuss the connections between our model and other methods like graph-based tools, and demonstrate that our algorithms enjoy the advantages of both global and local methods. Experimental results on both the Middleburry datasets and other captured datasets finally show that our method is able to improve the performances of depth images and multi-view depth videos recovery compared with state-of-the-art approaches.

IEEE2019

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Graph-Based Non-Uniform Sampling and Reconstruction of Depth Maps

Pascal Frossard, Kun Li, Jingyu Yang, Xinchen Ye

IEEE2019

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DepthInSpace: Exploitation and Fusion of Multiple Video Frames for Structured-Light Depth Estimation

Camilla Carta, François Fleuret

We present DepthInSpace, a self-supervised deep-learning method for depth estimation using a structured-light camera. The design of this method is motivated by the commercial use case of embedded depth sensors in nowadays smartphones. We first propose to use estimated optical flow from ambient information of multiple video frames as a complementary guide for training a single-frame depth estimation network, helping to preserve edges and reduce over-smoothing issues. Utilizing optical flow, we also propose to fuse the data of multiple video frames to get a more accurate depth map. In particular, fused depth maps are more robust in occluded areas and incur less in flying pixels artifacts. We finally demonstrate that these more precise fused depth maps can be used as self-supervision for fine-tuning a single-frame depth estimation network to improve its performance. Our models' effectiveness is evaluated and compared with state-of-the-art models on both synthetic and our newly introduced real datasets.

IEEE2021

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