View source for 17714347. METHOD AND APPARATUS WITH DEPTH MAP GENERATION simplified abstract (Samsung Electronics Co., Ltd.)

=METHOD AND APPARATUS WITH DEPTH MAP GENERATION=


==Organization Name==

[[:Category:Samsung Electronics Co., Ltd.|Samsung Electronics Co., Ltd.]]


[[Category:Samsung Electronics Co., Ltd.]]

==Inventor(s)==

[[:Category:Seokhwan Jang of Anyang-si (KR)|Seokhwan Jang of Anyang-si (KR)]][[Category:Seokhwan Jang of Anyang-si (KR)]]

[[:Category:SEUNGEON Kim of Incheon (KR)|SEUNGEON Kim of Incheon (KR)]][[Category:SEUNGEON Kim of Incheon (KR)]]

[[:Category:MINJUNG Son of Suwon-si (KR)|MINJUNG Son of Suwon-si (KR)]][[Category:MINJUNG Son of Suwon-si (KR)]]

==METHOD AND APPARATUS WITH DEPTH MAP GENERATION - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17714347 titled 'METHOD AND APPARATUS WITH DEPTH MAP GENERATION

==Simplified Explanation==

The patent application describes a method and apparatus for generating depth maps using multi-view depth maps and camera parameters. Here is a simplified explanation of the abstract:

* The method involves generating points for a point cloud by converting multi-view depth maps into a 3D space using camera parameters.
* Feature embedding vectors are extracted for the generated points.
* A 2D feature map is generated for a specific view based on the extracted feature embedding vectors.
* A refinement network is used to generate a residual depth map based on the 2D feature map.
* A new depth map is generated by combining the residual depth map with an initial depth map from the multi-view depth maps.

== Potential Applications ==
This technology has potential applications in various fields, including:

* Computer vision: The generated depth maps can be used for object recognition, scene understanding, and augmented reality applications.
* Robotics: Accurate depth maps can aid in robot navigation, obstacle avoidance, and object manipulation tasks.
* Autonomous vehicles: Depth maps can enhance the perception capabilities of autonomous vehicles, enabling better object detection and tracking.

== Problems Solved ==
The method and apparatus described in the patent application address the following problems:

* Depth map generation: The technology provides a method to generate accurate depth maps from multi-view depth maps, improving the understanding of the 3D structure of a scene.
* Feature extraction: The method extracts feature embedding vectors to represent the points in the generated point cloud, enabling efficient analysis and processing of the data.
* Depth map refinement: The use of a refinement network helps to enhance the quality of the depth maps, reducing noise and improving accuracy.

== Benefits ==
The use of this technology offers several benefits:

* Improved depth map accuracy: By combining multiple views and utilizing camera parameters, the generated depth maps are more accurate and reliable.
* Efficient processing: The extraction of feature embedding vectors and the use of a refinement network optimize the processing of depth map generation.
* Versatility: The method can be applied to various scenarios and applications, providing flexibility in utilizing depth maps for different purposes.


==Original Abstract Submitted==

A method and apparatus with depth map generation. The method may include generating points for a point cloud by unprojecting multi-view depth maps, of plural views, into a corresponding three-dimensional (3D) space using respective camera parameters corresponding to each view of the multi-view depth maps, extracting feature embedding vectors corresponding to the generated points, generating a two-dimensional (2D) feature map of a set view based on the extracted feature embedding vectors, generating a residual depth map using a refinement network with respect to the 2D feature map, generating a new depth map based on the residual depth map and an initial depth map, of the set view, among the multi-view depth maps.



[[Category:G06T7/55]]
[[Category:G06T7/73]]