Journal of the Institute of Electronics Engineers of Korea SP (대한전자공학회논문지SP)

The Institute of Electronics and Information Engineers (대한전자공학회)
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On Shape Recovery of 3D Object from Multiple Range Images

시점이 다른 다수의 거리 영상으로부터 3차원 물체의 형상 복원

  • Published : 2000.01.25
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Abstract

To reconstruct 3- D shape, It is a common strategy to acquire multiple range Images from different viewpoints and integrate them into a common coordinates In this paper, we particularly focus on the registration and integration processes for combining all range Images into one surface model. For the registration, we propose the 2-step registration algorithm, which consists of 2 steps the rough registration step using all data points and the fine registration step using the high-curved data points For the integration, we propose a new algorithm, referred to as ‘multi-registration’ technique, to alleviate the error accumulation problem, which occurs during applying the pair-wise registration to each range image sequentially, in order to transform them into a common reference frame Intensive experiments are performed on the various real range data In experiments, all range images were registered within 1 minutes on Pentium 150MHz PC The results show that the proposed algorithms registrate and integrate multiple range Images within a tolerable error bound in a reasonable computation time, and the total error between all range Images are equalized with our proposed algorithms.

3차원 형상의 복구를 위하여 일반적으로 다른 방향에서 취득한 거리 영상을 하나의 좌표계로 변환하는 과정이 필요하다. 본 논문에서는 이러한 과정 중 레지스트레이션과 인티그레이션에 관한 연구 결과를 제시한다 레지스트레이션에 대해서는 가존의 ICP 알고리듬의 수렴 속도 향상을 위해 높은 곡률을 갖는 데이터에 가중치를 주는 2 단계의 알고리듬을 제안한다 첫 번째 단계에서는 모든 거리 데이터 이용하여 적당한 정도의 레지스트레이션을 수행하고 두 번째 단계에서 높은 곡률을 가진 점들만을 이용하여 보다 정확한 레지스트레이션을 수행한다 인티그레이션 알고리듬으로는 가준 좌표계로 변환된 모든 거리 영상간의 전체 오차를 최소화하기 위해 2장간의 거리 영상에 대한 ICP 알고리듬을 임의의 N장의 거리 영상에 대하여 적용할 수 있도록 일반화하는 알고리듬을 제안하였다 제안하는 알고리듬을 통하여 2장간의 변환을 순차적으로 수행하여 기준 좌표계로의 변환을 얻었을 때 레지스트레이션 오차가 누적되는 문제점을 해결하였다 실험결과 레지스트레이션 알고리듬은 펜티움 150MHz PC 환경에서 l분정도의 수행시간을 나타내었다 실험 결과는 제안하는 기법이 적절한 시간내에 모든 거리 데이터들을 오차가 고르게 분포하는 모델을 형성할 수 있음을 보인다.

Keywords

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