4 research outputs found

    Static/Dynamic Filtering for Mesh Geometry

    Get PDF
    The joint bilateral filter, which enables feature-preserving signal smoothing according to the structural information from a guidance, has been applied for various tasks in geometry processing. Existing methods either rely on a static guidance that may be inconsistent with the input and lead to unsatisfactory results, or a dynamic guidance that is automatically updated but sensitive to noises and outliers. Inspired by recent advances in image filtering, we propose a new geometry filtering technique called static/dynamic filter, which utilizes both static and dynamic guidances to achieve state-of-the-art results. The proposed filter is based on a nonlinear optimization that enforces smoothness of the signal while preserving variations that correspond to features of certain scales. We develop an efficient iterative solver for the problem, which unifies existing filters that are based on static or dynamic guidances. The filter can be applied to mesh face normals followed by vertex position update, to achieve scale-aware and feature-preserving filtering of mesh geometry. It also works well for other types of signals defined on mesh surfaces, such as texture colors. Extensive experimental results demonstrate the effectiveness of the proposed filter for various geometry processing applications such as mesh denoising, geometry feature enhancement, and texture color filtering

    Nonlinear Spectral Geometry Processing via the TV Transform

    Full text link
    We introduce a novel computational framework for digital geometry processing, based upon the derivation of a nonlinear operator associated to the total variation functional. Such operator admits a generalized notion of spectral decomposition, yielding a sparse multiscale representation akin to Laplacian-based methods, while at the same time avoiding undesirable over-smoothing effects typical of such techniques. Our approach entails accurate, detail-preserving decomposition and manipulation of 3D shape geometry while taking an especially intuitive form: non-local semantic details are well separated into different bands, which can then be filtered and re-synthesized with a straightforward linear step. Our computational framework is flexible, can be applied to a variety of signals, and is easily adapted to different geometry representations, including triangle meshes and point clouds. We showcase our method throughout multiple applications in graphics, ranging from surface and signal denoising to detail transfer and cubic stylization.Comment: 16 pages, 20 figure

    Localization in Low Luminance, Slippery Indoor Environment Using Afocal Optical Flow Sensor and Image Processing

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€, 2017. 8. ์กฐ๋™์ผ.์‹ค๋‚ด ์„œ๋น„์Šค๋กœ๋ด‡์˜ ์œ„์น˜ ์ถ”์ •์€ ์ž์œจ ์ฃผํ–‰์„ ์œ„ํ•œ ํ•„์ˆ˜ ์š”๊ฑด์ด๋‹ค. ํŠนํžˆ ์นด๋ฉ”๋ผ๋กœ ์œ„์น˜๋ฅผ ์ถ”์ •ํ•˜๊ธฐ ์–ด๋ ค์šด ์‹ค๋‚ด ์ €์กฐ๋„ ํ™˜๊ฒฝ์—์„œ ๋ฏธ๋„๋Ÿฌ์ง์ด ๋ฐœ์ƒํ•  ๊ฒฝ์šฐ์—๋Š” ์œ„์น˜ ์ถ”์ •์˜ ์ •ํ™•๋„๊ฐ€ ๋‚ฎ์•„์ง„๋‹ค. ๋ฏธ๋„๋Ÿฌ์ง์€ ์ฃผ๋กœ ์นดํŽซ์ด๋‚˜ ๋ฌธํ„ฑ ๋“ฑ์„ ์ฃผํ–‰ํ•  ๋•Œ ๋ฐœ์ƒํ•˜๋ฉฐ, ํœ  ์—”์ฝ”๋” ๊ธฐ๋ฐ˜์˜ ์ฃผํ–‰๊ธฐ๋ก์œผ๋กœ๋Š” ์ฃผํ–‰ ๊ฑฐ๋ฆฌ์˜ ์ •ํ™•ํ•œ ์ธ์‹์— ํ•œ๊ณ„๊ฐ€ ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์นด๋ฉ”๋ผ ๊ธฐ๋ฐ˜ ๋™์‹œ์  ์œ„์น˜์ถ”์ • ๋ฐ ์ง€๋„์ž‘์„ฑ ๊ธฐ์ˆ (simultaneous localization and mappingSLAM)์ด ๋™์ž‘ํ•˜๊ธฐ ์–ด๋ ค์šด ์ €์กฐ๋„, ๋ฏธ๋„๋Ÿฌ์šด ํ™˜๊ฒฝ์—์„œ ์ €๊ฐ€์˜ ๋ชจ์…˜์„ผ์„œ์™€ ๋ฌดํ•œ์ดˆ์  ๊ด‘ํ•™ํ๋ฆ„์„ผ์„œ(afocal optical flow sensorAOFS) ๋ฐ VGA๊ธ‰ ์ „๋ฐฉ ๋‹จ์•ˆ์นด๋ฉ”๋ผ๋ฅผ ์œตํ•ฉํ•˜์—ฌ ๊ฐ•์ธํ•˜๊ฒŒ ์œ„์น˜๋ฅผ ์ถ”์ •ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ–ˆ๋‹ค. ๋กœ๋ด‡์˜ ์œ„์น˜ ์ถ”์ •์€ ์ฃผํ–‰๊ฑฐ๋ฆฌ ์ˆœ๊ฐ„ ๋ณ€ํ™”๋Ÿ‰๊ณผ ๋ฐฉ์œ„๊ฐ ์ˆœ๊ฐ„ ๋ณ€ํ™”๋Ÿ‰์„ ๋ˆ„์  ์œตํ•ฉํ•˜์—ฌ ์‚ฐ์ถœํ–ˆ์œผ๋ฉฐ, ๋ฏธ๋„๋Ÿฌ์šด ํ™˜๊ฒฝ์—์„œ๋„ ์ข€ ๋” ์ •ํ™•ํ•œ ์ฃผํ–‰๊ฑฐ๋ฆฌ ์ถ”์ •์„ ์œ„ํ•ด ํœ  ์—”์ฝ”๋”์™€ AOFS๋กœ๋ถ€ํ„ฐ ํš๋“ํ•œ ์ด๋™ ๋ณ€์œ„ ์ •๋ณด๋ฅผ ์œตํ•ฉํ–ˆ๊ณ , ๋ฐฉ์œ„๊ฐ ์ถ”์ •์„ ์œ„ํ•ด ๊ฐ์†๋„ ์„ผ์„œ์™€ ์ „๋ฐฉ ์˜์ƒ์œผ๋กœ๋ถ€ํ„ฐ ํŒŒ์•…๋œ ์‹ค๋‚ด ๊ณต๊ฐ„์ •๋ณด๋ฅผ ํ™œ์šฉํ–ˆ๋‹ค. ๊ด‘ํ•™ํ๋ฆ„์„ผ์„œ๋Š” ๋ฐ”ํ€ด ๋ฏธ๋„๋Ÿฌ์ง์— ๊ฐ•์ธํ•˜๊ฒŒ ์ด๋™ ๋ณ€์œ„๋ฅผ ์ถ”์ • ํ•˜์ง€๋งŒ, ์นดํŽซ์ฒ˜๋Ÿผ ํ‰ํ‰ํ•˜์ง€ ์•Š์€ ํ‘œ๋ฉด์„ ์ฃผํ–‰ํ•˜๋Š” ์ด๋™ ๋กœ๋ด‡์— ๊ด‘ํ•™ํ๋ฆ„์„ผ์„œ๋ฅผ ์žฅ์ฐฉํ•  ๊ฒฝ์šฐ, ์ฃผํ–‰ ์ค‘ ๋ฐœ์ƒํ•˜๋Š” ๊ด‘ํ•™ํ๋ฆ„์„ผ์„œ์™€ ๋ฐ”๋‹ฅ ๊ฐ„์˜ ๋†’์ด ๋ณ€ํ™”๊ฐ€ ๊ด‘ํ•™ํ๋ฆ„์„ผ์„œ๋ฅผ ์ด์šฉํ•œ ์ด๋™๊ฑฐ๋ฆฌ ์ถ”์ • ์˜ค์ฐจ์˜ ์ฃผ์š”์ธ์œผ๋กœ ์ž‘์šฉํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๊ด‘ํ•™ํ๋ฆ„์„ผ์„œ์— ๋ฌดํ•œ์ดˆ์ ๊ณ„ ์›๋ฆฌ๋ฅผ ์ ์šฉํ•˜์—ฌ ์ด ์˜ค์ฐจ ์š”์ธ์„ ์™„ํ™”ํ•˜๋Š” ๋ฐฉ์•ˆ์„ ์ œ์‹œํ•˜์˜€๋‹ค. ๋กœ๋ด‡ ๋ฌธํ˜• ์‹œ์Šคํ…œ(robotic gantry system)์„ ์ด์šฉํ•˜์—ฌ ์นดํŽซ ๋ฐ ์„ธ๊ฐ€์ง€ ์ข…๋ฅ˜์˜ ๋ฐ”๋‹ฅ์žฌ์งˆ์—์„œ ๊ด‘ํ•™ํ๋ฆ„์„ผ์„œ์˜ ๋†’์ด๋ฅผ 30 mm ์—์„œ 50 mm ๋กœ ๋ณ€ํ™”์‹œํ‚ค๋ฉฐ 80 cm ๊ฑฐ๋ฆฌ๋ฅผ ์ด๋™ํ•˜๋Š” ์‹คํ—˜์„ 10๋ฒˆ์”ฉ ๋ฐ˜๋ณตํ•œ ๊ฒฐ๊ณผ, ๋ณธ ๋…ผ๋ฌธ์—์„œ ์ œ์•ˆํ•˜๋Š” AOFS ๋ชจ๋“ˆ์€ 1 mm ๋†’์ด ๋ณ€ํ™” ๋‹น 0.1% ์˜ ๊ณ„ํ†ต์˜ค์ฐจ(systematic error)๋ฅผ ๋ฐœ์ƒ์‹œ์ผฐ์œผ๋‚˜, ๊ธฐ์กด์˜ ๊ณ ์ •์ดˆ์ ๋ฐฉ์‹์˜ ๊ด‘ํ•™ํ๋ฆ„์„ผ์„œ๋Š” 14.7% ์˜ ๊ณ„ํ†ต์˜ค์ฐจ๋ฅผ ๋‚˜ํƒ€๋ƒˆ๋‹ค. ์‹ค๋‚ด ์ด๋™์šฉ ์„œ๋น„์Šค ๋กœ๋ด‡์— AOFS๋ฅผ ์žฅ์ฐฉํ•˜์—ฌ ์นดํŽซ ์œ„์—์„œ 1 m ๋ฅผ ์ฃผํ–‰ํ•œ ๊ฒฐ๊ณผ ํ‰๊ท  ๊ฑฐ๋ฆฌ ์ถ”์ • ์˜ค์ฐจ๋Š” 0.02% ์ด๊ณ , ๋ถ„์‚ฐ์€ 17.6% ์ธ ๋ฐ˜๋ฉด, ๊ณ ์ •์ดˆ์  ๊ด‘ํ•™ํ๋ฆ„์„ผ์„œ๋ฅผ ๋กœ๋ด‡์— ์žฅ์ฐฉํ•˜์—ฌ ๊ฐ™์€ ์‹คํ—˜์„ ํ–ˆ์„ ๋•Œ์—๋Š” 4.09% ์˜ ํ‰๊ท  ์˜ค์ฐจ ๋ฐ 25.7% ์˜ ๋ถ„์‚ฐ์„ ๋‚˜ํƒ€๋ƒˆ๋‹ค. ์ฃผ์œ„๊ฐ€ ๋„ˆ๋ฌด ์–ด๋‘์›Œ์„œ ์˜์ƒ์„ ์œ„์น˜ ๋ณด์ •์— ์‚ฌ์šฉํ•˜๊ธฐ ์–ด๋ ค์šด ๊ฒฝ์šฐ, ์ฆ‰, ์ €์กฐ๋„ ์˜์ƒ์„ ๋ฐ๊ฒŒ ๊ฐœ์„ ํ–ˆ์œผ๋‚˜ SLAM์— ํ™œ์šฉํ•  ๊ฐ•์ธํ•œ ํŠน์ง•์  ํ˜น์€ ํŠน์ง•์„ ์„ ์ถ”์ถœํ•˜๊ธฐ ์–ด๋ ค์šด ๊ฒฝ์šฐ์—๋„ ๋กœ๋ด‡ ์ฃผํ–‰ ๊ฐ๋„ ๋ณด์ •์— ์ €์กฐ๋„ ์ด๋ฏธ์ง€๋ฅผ ํ™œ์šฉํ•˜๋Š” ๋ฐฉ์•ˆ์„ ์ œ์‹œํ–ˆ๋‹ค. ์ €์กฐ๋„ ์˜์ƒ์— ํžˆ์Šคํ† ๊ทธ๋žจ ํ‰ํ™œํ™”(histogram equalization) ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ ์šฉํ•˜๋ฉด ์˜์ƒ์ด ๋ฐ๊ฒŒ ๋ณด์ • ๋˜๋ฉด์„œ ๋™์‹œ์— ์žก์Œ๋„ ์ฆ๊ฐ€ํ•˜๊ฒŒ ๋˜๋Š”๋ฐ, ์˜์ƒ ์žก์Œ์„ ์—†์• ๋Š” ๋™์‹œ์— ์ด๋ฏธ์ง€ ๊ฒฝ๊ณ„๋ฅผ ๋šœ๋ ทํ•˜๊ฒŒ ํ•˜๋Š” ๋กค๋ง ๊ฐ€์ด๋˜์Šค ํ•„ํ„ฐ(rolling guidance filterRGF)๋ฅผ ์ ์šฉํ•˜์—ฌ ์ด๋ฏธ์ง€๋ฅผ ๊ฐœ์„ ํ•˜๊ณ , ์ด ์ด๋ฏธ์ง€์—์„œ ์‹ค๋‚ด ๊ณต๊ฐ„์„ ๊ตฌ์„ฑํ•˜๋Š” ์ง๊ต ์ง์„  ์„ฑ๋ถ„์„ ์ถ”์ถœ ํ›„ ์†Œ์‹ค์ (vanishing pointVP)์„ ์ถ”์ •ํ•˜๊ณ  ์†Œ์‹ค์ ์„ ๊ธฐ์ค€์œผ๋กœ ํ•œ ๋กœ๋ด‡ ์ƒ๋Œ€ ๋ฐฉ์œ„๊ฐ์„ ํš๋“ํ•˜์—ฌ ๊ฐ๋„ ๋ณด์ •์— ํ™œ์šฉํ–ˆ๋‹ค. ์ œ์•ˆํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ๋กœ๋ด‡์— ์ ์šฉํ•˜์—ฌ 0.06 ~ 0.21 lx ์˜ ์ €์กฐ๋„ ์‹ค๋‚ด ๊ณต๊ฐ„(77 sqm)์— ์นดํŽซ์„ ์„ค์น˜ํ•˜๊ณ  ์ฃผํ–‰ํ–ˆ์„ ๊ฒฝ์šฐ, ๋กœ๋ด‡์˜ ๋ณต๊ท€ ์œ„์น˜ ์˜ค์ฐจ๊ฐ€ ๊ธฐ์กด 401 cm ์—์„œ 21 cm๋กœ ์ค„์–ด๋“ฆ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค.์ œ 1 ์žฅ ์„œ ๋ก  1 1.1 ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ 1 1.2 ์„ ํ–‰ ์—ฐ๊ตฌ ์กฐ์‚ฌ 6 1.2.1 ์‹ค๋‚ด ์ด๋™ํ˜• ์„œ๋น„์Šค ๋กœ๋ด‡์˜ ๋ฏธ๋„๋Ÿฌ์ง ๊ฐ์ง€ ๊ธฐ์ˆ  6 1.2.2 ์ €์กฐ๋„ ์˜์ƒ ๊ฐœ์„  ๊ธฐ์ˆ  8 1.3 ๊ธฐ์—ฌ๋„ 12 1.4 ๋…ผ๋ฌธ์˜ ๊ตฌ์„ฑ 14 ์ œ 2 ์žฅ ๋ฌดํ•œ์ดˆ์  ๊ด‘ํ•™ํ๋ฆ„์„ผ์„œ(AOFS) ๋ชจ๋“ˆ 16 2.1 ๋ฌดํ•œ์ดˆ์  ์‹œ์Šคํ…œ(afocal system) 16 2.2 ๋ฐ”๋Š˜๊ตฌ๋ฉ ํšจ๊ณผ 18 2.3 ๋ฌดํ•œ์ดˆ์  ๊ด‘ํ•™ํ๋ฆ„์„ผ์„œ(AOFS) ๋ชจ๋“ˆ ํ”„๋กœํ† ํƒ€์ž… 20 2.4 ๋ฌดํ•œ์ดˆ์  ๊ด‘ํ•™ํ๋ฆ„์„ผ์„œ(AOFS) ๋ชจ๋“ˆ ์‹คํ—˜ ๊ณ„ํš 24 2.5 ๋ฌดํ•œ์ดˆ์  ๊ด‘ํ•™ํ๋ฆ„์„ผ์„œ(AOFS) ๋ชจ๋“ˆ ์‹คํ—˜ ๊ฒฐ๊ณผ 29 ์ œ 3 ์žฅ ์ €์กฐ๋„์˜์ƒ์˜ ๋ฐฉ์œ„๊ฐ๋ณด์ • ํ™œ์šฉ๋ฐฉ๋ฒ• 36 3.1 ์ €์กฐ๋„ ์˜์ƒ ๊ฐœ์„  ๋ฐฉ๋ฒ• 36 3.2 ํ•œ ์žฅ์˜ ์˜์ƒ์œผ๋กœ ์‹ค๋‚ด ๊ณต๊ฐ„ ํŒŒ์•… ๋ฐฉ๋ฒ• 38 3.3 ์†Œ์‹ค์  ๋žœ๋“œ๋งˆํฌ๋ฅผ ์ด์šฉํ•œ ๋กœ๋ด‡ ๊ฐ๋„ ์ถ”์ • 41 3.4 ์ตœ์ข… ์ฃผํ–‰๊ธฐ๋ก ์•Œ๊ณ ๋ฆฌ์ฆ˜ 46 3.5 ์ €์กฐ๋„์˜์ƒ์˜ ๋ฐฉ์œ„๊ฐ ๋ณด์ • ์‹คํ—˜ ๊ณ„ํš 48 3.6 ์ €์กฐ๋„์˜์ƒ์˜ ๋ฐฉ์œ„๊ฐ ๋ณด์ • ์‹คํ—˜ ๊ฒฐ๊ณผ 50 ์ œ 4 ์žฅ ์ €์กฐ๋„ ํ™˜๊ฒฝ ์œ„์น˜์ธ์‹ ์‹คํ—˜ ๊ฒฐ๊ณผ 54 4.1 ์‹คํ—˜ ํ™˜๊ฒฝ 54 4.2 ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ์‹คํ—˜ ๊ฒฐ๊ณผ 59 4.3 ์ž„๋ฒ ๋””๋“œ ์‹คํ—˜ ๊ฒฐ๊ณผ 61 ์ œ 5 ์žฅ ๊ฒฐ๋ก  62Docto

    Rolling guidance normal filter for geometric processing

    No full text
    corecore