{"id":172662,"date":"2019-10-10T16:41:08","date_gmt":"2019-10-10T23:41:08","guid":{"rendered":"https:\/\/thinklucid.com\/time-of-flight-camera-guidebook\/"},"modified":"2024-09-05T12:58:03","modified_gmt":"2024-09-05T19:58:03","slug":"time-of-flight-camera-guidebook","status":"publish","type":"page","link":"https:\/\/thinklucid.com\/ja\/time-of-flight-camera-guidebook\/","title":{"rendered":"LUCID Time of Flight Guidebook 2.0"},"content":{"rendered":"<div class=\"wpb-content-wrapper\"><p>[vc_row full_width=\u201dstretch_row_content_no_spaces\u201d gap=\u201d35\u2033][vc_column offset=\u201dvc_col-lg-offset-0 vc_col-lg-7 vc_col-md-offset-1 vc_col-md-6 vc_col-sm-offset-0 vc_col-xs-12\u2033][vc_row_inner][vc_column_inner width=\u201d1\/6\u2033][\/vc_column_inner][vc_column_inner width=\u201d5\/6\u2033][vc_custom_heading text=\u201dPDF\u30ac\u30a4\u30c9\u30d6\u30c3\u30af\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u201d font_container=\u201dtag:h2|font_size:26px|text_align:left\u201d use_theme_fonts=\u201dyes\u201d css=\u201d.vc_custom_1725559480936{margin-bottom: 0px !important;}\u201d][vc_custom_heading text=\u201d<strong>Time-of-Flight Guidebook 2.0:<\/strong> 3D\u6027\u80fd\u3092\u5411\u4e0a\u3055\u305b\u3001\u8a2d\u8a08\u306b\u304b\u304b\u308b\u6642\u9593\u3068\u30b3\u30b9\u30c8\u3092\u524a\u6e1b\u3059\u308b\u30d2\u30f3\u30c8\u201d font_container=\u201dtag:h2|font_size:36px|text_align:left\u201d use_theme_fonts=\u201dyes\u201d css=\u201d.vc_custom_1725559492757{margin-top: 25px !important;}\u201d][\/vc_column_inner][\/vc_row_inner][vc_row_inner equal_height=\u201dyes\u201d content_placement=\u201dtop\u201d css=\u201d.vc_custom_1570662605011{margin-top: 15px !important;}\u201d][vc_column_inner width=\u201d5\/12\u2033 offset=\u201dvc_col-md-offset-1\u2033][vc_single_image image=\u201d172508\u2033 img_size=\u201dfull\u201d alignment=\u201dcenter\u201d css_animation=\u201dnone\u201d css=\u201d.vc_custom_1724712497117{margin-bottom: 10px !important;}\u201d][\/vc_column_inner][vc_column_inner width=\u201d1\/2\u2033][vc_column_text]<\/p>\n<p style=\"line-height: 1.8;\">3D\u98db\u884c\u6642\u9593\u578b\u753b\u50cf\u51e6\u7406\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u306e\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u3092\u5411\u4e0a\u3055\u305b\u3001\u540c\u6642\u306b\u30a4\u30f3\u30c6\u30b0\u30ec\u30fc\u30b7\u30e7\u30f3\u306b\u304b\u304b\u308b\u6642\u9593\u3068\u30b3\u30b9\u30c8\u3092\u524a\u6e1b\u3059\u308b\u65b9\u6cd5\u3092\u3054\u5b58\u77e5\u3067\u3059\u304b\uff1fHelios2\u306e\u30cf\u30f3\u30ba\u30aa\u30f3\u30ac\u30a4\u30c9\u30d6\u30c3\u30af\u3067\u306f\u3001\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u306e\u74b0\u5883\u3084\u30b7\u30fc\u30f3\u5185\u306e\u5bfe\u8c61\u7269\u306e\u7279\u6027\u306a\u3069\u3001\u3055\u307e\u3056\u307e\u306a\u8981\u56e0\u3092\u8003\u616e\u3059\u308b\u3053\u3068\u3067\u3001\u98db\u884c\u6642\u9593\u8a08\u306e\u5229\u70b9\u3092\u6700\u9069\u5316\u3059\u308b\u65b9\u6cd5\u3092\u8aac\u660e\u3057\u3066\u3044\u307e\u3059\u3002\u307e\u305f\u3001\u3055\u307e\u3056\u307e\u306aHelios2\u30e2\u30c7\u30eb\u304c\u30013D\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u306e\u4e00\u822c\u7684\u306a\u8ab2\u984c\u3092\u3069\u306e\u3088\u3046\u306b\u89e3\u6c7a\u3059\u308b\u304b\u3082\u3054\u89a7\u304f\u3060\u3055\u3044\u3002<\/p>\n<p style=\"color: black; text-align: left;\"><strong>\u76ee\u6b21:<\/strong><\/p>\n<p style=\"color: black; text-align: left; line-height: 1.5; padding-left: 30px;\"><strong>\u2022 \u5c4b\u5916\u3068\u7ba1\u7406\u3055\u308c\u305f\u74b0\u5883\u306e\u6bd4\u8f03<\/strong><br \/>\n<strong>\u2022 \u88ab\u5199\u4f53\u3068\u30ab\u30e1\u30e9\u306e\u30e2\u30fc\u30b7\u30e7\u30f3<\/strong><br \/>\n<strong>\u2022 \u9ad8\u53cd\u5c04\u3001\u4f4e\u53cd\u5c04\u304a\u3088\u3073\u900f\u660e\u306a\u5bfe\u8c61\u7269<\/strong><br \/>\n<strong>\u2022 \u30b7\u30fc\u30f3\u306e\u8907\u96d1\u3055\u3092\u7c21\u7d20\u5316\u3059\u308b<\/strong><br \/>\n<strong>\u2022 Working Distance\u306e\u8003\u616e<\/strong><br \/>\n<strong>\u2022 \u30ab\u30e1\u30e9\u6a5f\u80fd\u306e\u62e1\u5f35<\/strong><br \/>\n<strong>\u2022 \u307e\u3068\u3081<\/strong><\/p>\n<div style=\"color: black; line-height: 1.5; text-align: center;\"><strong><a href=\"#sample-chapter\"><em>\u7b2c3\u7ae0 \u300c\u9ad8\u53cd\u5c04\u3001\u4f4e\u53cd\u5c04\u304a\u3088\u3073\u900f\u660e\u306a\u5bfe\u8c61\u7269 \u300d\u306e\u30b5\u30f3\u30d7\u30eb\u30da\u30fc\u30b8\u306f\u4e0b\u306b\u30b9\u30af\u30ed\u30fc\u30eb\u3057\u3066\u304f\u3060\u3055\u3044\u3002-<\/em><\/a><\/strong><\/div>\n<p>[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][\/vc_column][vc_column width=\u201d5\/6\u2033 offset=\u201dvc_col-lg-offset-0 vc_col-lg-4 vc_col-md-offset-0 vc_col-md-4 vc_col-sm-offset-1 vc_col-xs-12\u2033 css=\u201d.vc_custom_1570665449993{margin-top: 50px !important;}\u201d el_id=\u201dform-top\u201d][vc_column_text]<span style=\"font-size: 14px;\">\u9001\u4fe1\u300d\u3092\u30af\u30ea\u30c3\u30af\u3059\u308b\u3068\u3001\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u30ea\u30f3\u30af\u304c\u8868\u793a\u3055\u308c\u307e\u3059\u3002<\/span><\/p>\n<p>[\/vc_column_text][vc_column_text]<\/p>\n<iframe height=\"740px\" width=\"100%\" frameborder=\"0\" allow=\"geolocation;microphone;camera\" src=\"https:\/\/forms.zohopublic.com\/webmaster40\/form\/TimeofFlightGuidebookDownloadForm\/formperma\/Z7u5Hvbw5sv25wqhRnXKnR22kma7l1ui_Tnwk6A8Y5I?zf_lang=ja\"> <\/iframe>\n<p>[\/vc_column_text][\/vc_column][\/vc_row][vc_row full_width=\u201dstretch_row_content\u201d css=\u201d.vc_custom_1570749000977{background-color: #f2f2f2 !important;}\u201d el_id=\u201dsample-chapter\u201d][vc_column css=\u201d.vc_custom_1570665612734{margin-top: 25px !important;}\u201d][vc_column_text]<\/p>\n<p style=\"color: black; text-align: center; font-size: x-large;\">\u2013 \u30b5\u30f3\u30d7\u30eb\u7ae0 \u2013<\/p>\n<p>[\/vc_column_text][\/vc_column][\/vc_row][vc_row full_width=\u201dstretch_row_content\u201d css=\u201d.vc_custom_1570749000977{background-color: #f2f2f2 !important;}\u201d][vc_column css=\u201d.vc_custom_1570665612734{margin-top: 25px !important;}\u201d]\n\t\t\t\t<p class=\"rs-p-wp-fix\"><\/p>\n\t\t\t\t<sr7-module data-alias=\"slider-29\" data-id=\"142\" id=\"SR7_142_1\" data-version=\"7.1.1\">\n\t\t\t\t\t<sr7-adjuster><\/sr7-adjuster>\n\t\t\t\t\t<sr7-content>\n\t\t\t\t\t\t<sr7-slide id=\"SR7_142_1-340\" data-key=\"340\">\n\t\t\t\t\t\t\t<sr7-layer id=\"SR7_142_1-340-0\">SPECULAR, DIFFUSE & TRANSPARENT TARGETS<\/sr7-layer>\n\t\t\t\t\t\t\t<sr7-layer id=\"SR7_142_1-340-1\">ToF works best on targets with surfaces that have neither high specular reflection (i.e., mirror-like) nor absorbent non-reflective surfaces. Both extremes lead to point cloud distortions. For example, black PVC piping has both low reflectivity (black color) and highly specular surfaces, creating both voids and spikes in the point cloud (Image Set 4, Example c.) Targets that are black or very dark will absorb light and thus return the least amount of light creating voids or holes in the point cloud. Targets with high specular reflections can saturate pixels creating spikes in the point cloud that are closer in distance than their true position. Targets with these properties may not be suitable for ToF. \t<br \/>\n\t<\/sr7-layer>\n\t\t\t\t\t\t\t<sr7-layer id=\"SR7_142_1-340-2\"><strong>Targets with diffuse surfaces and high reflectivity work best for ToF (Image Set 4, Example a, b, d).<\/strong> These targets send enough light back to the ToF sensor without specular reflections. Some objects however, exhibit properties that are less than ideal but are still discernible within the scene. 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Shorter exposure time and normal gain is used for scenes closer to the camera, or objects that appear over saturated.<\/sr7-layer>\n\t\t\t\t\t\t\t<sr7-layer id=\"SR7_142_1-340-4\">EXPOSURE TIME AND GAIN<\/sr7-layer>\n\t\t\t\t\t\t\t<sr7-layer id=\"SR7_142_1-340-5\">IMAGE ACCUMULATION<\/sr7-layer>\n\t\t\t\t\t\t\t<sr7-layer id=\"SR7_142_1-340-6\">The Helios2 processing pipeline is capable of accumulating multiple frames for improved depth calculations. This is helpful for targets that produce noisy data. With image accumulation, depth frames are averaged over a set number of frames, improving imaging results. It should be noted that the higher the number of frames accumulated, the slower the depth data generation, as more images must be captured to calculate the data.<\/sr7-layer>\n\t\t\t\t\t\t\t<sr7-layer id=\"SR7_142_1-340-7\">CONFIDENCE THRESHOLD<\/sr7-layer>\n\t\t\t\t\t\t\t<sr7-layer id=\"SR7_142_1-340-8\">Depth data confidence is based on a level of intensity for each point. If a returning signal is too weak, there is low confidence in the resulting depth data. Thresholding removes depth data with low intensity\/confidence, improving scene clarity.<\/sr7-layer>\n\t\t\t\t\t\t\t<sr7-layer id=\"SR7_142_1-340-9\">SPATIAL FILTERING<\/sr7-layer>\n\t\t\t\t\t\t\t<sr7-layer id=\"SR7_142_1-340-10\">Spatial filtering reduces noise by adjusting and averaging depth data differences between neighboring pixels\u2014smoothing out surfaces. Additionally, the Helios camera also uses edge-preservation in its spatial filtering, reducing noise in surfaces while maintaining object-edge sharpness.<br \/>\n<\/sr7-layer>\n\t\t\t\t\t\t\t<sr7-layer id=\"SR7_142_1-340-11\">The Helios2 processing pipeline is capable of accumulating multiple frames for improved depth calculations. This is helpful for targets that produce noisy data. With image accumulation, depth frames are averaged over a set number of frames, improving imaging results. 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