Denser Feature Correspondences for 3D Reconstruction
| 言語 | 英語 |
|---|---|
| 種類 | 研究会等の予稿 |
| 著者 | Fairuz Safwan Mahad, Masakazu Iwamura, Yuzuko Utsumi, Koichi Kise |
| 論文名 | Denser Feature Correspondences for 3D Reconstruction |
| 論文誌名 | IPSJ SIG-CVIM: Computer Vision and Image Media |
| Vol. | 2016-CVIM-202 |
| No. | 3 |
| ページ | pp.1-5 |
| 発表場所 | Ritsumeikan University |
| 査読の有無 | 無 |
| 発表の種類 | 口頭ならびにポスター発表 |
| 年月 | 2016年5月 |
| 分野 | 三次元再構築 |
| URL | http://id.nii.ac.jp/1001/00159114/ |
| キー | Mahad2016_CVIM (id=1526) |
要約
The 3D reconstruction of an object is heavily dependent on the amount of texture on the surface of the object and the correspondences acquired between images from different viewpoints. Objects with lack of texture affects the reconstructed 3D model degrading its quality and completeness. This occurs due to the SIFTs and Harris's incapability of extracting sufficient features from regions with less texture thus resulting in a lack of correspondences in such regions. Gaps occurs in the reconstructed 3D model due to the lack of correspondences. The paper aims to obtain more correspondences through the implementation of convolution in conjunction with a set of randomized kernels. The use of randomized kernels changes the structure of the images in different ways allowing SIFT to extract features in less textured regions which could not be realized conventionally. Experimental results showed that the proposed method is able to fill up more gaps improving the completeness of the 3D model of less textured objects.
注記
関連ファイル
- …
この文献を出力
書式の定義
書式の定義の書き方
{authors} のように波括弧でフィールドを挿入します．[ ... ] で囲んだ部分は，その中のフィールドが空のとき丸ごと省略されます．使えるフィールド：
{authors}{authors_and}{authors_ja}{authors_ja_tight}{authors_en}{authors_en_and}{authors_tight}{first_author}{title}{title_ja}{title_en}{journal}{journal_ja}{journal_en}{book_title}{volume}{number}{series}{presen_id}{pages}{pp}{num_pages}{year}{month}{month_en}{month_short}{ym}{date}{date_ja}{date_en}{publisher}{address}{location}{review}{review_ja}{review_en}{reviewed_mark}{presen_type}{type}{type_label}{doi}{url}{key}{awards}