3-D pose estimation and model refinement of an articulated object from a monocular image sequence
This paper proposes a method to precisely estimate both the shape (link lengths) and pose (joint angles) of a articulated object from a monocular image sequence. Normal model-fitting method often leads to wrong estimates due to depth ambiguity in monocular views. The paper proposes a filtering method with constraint knowledge of the object represented as inequalities. The method calculates the probability distribution satisfying both the observation and the constraints. When multiple solutions are possible, they are preserved until a unique solution is determined. Experimental results show that the depth ambiguity is incrementally reduced if the informative observations are obtained.
Keywords3-D reconstruction articulated object depth ambiguity inequality constraints
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