It has the virtue of examining pose estimation of human-like limbs located in many positions in the scene, some flat on the ground, whereas others could be elevated on one side or float above the ground. 1 D was based on a painting by Phillip Pearlstein that appears to change a lot with viewpoint. 1 D, the doll in front always points toward the observer, even when the viewpoint shifts by 120°. The entire scene seemingly rotates with the viewpoint, so that perceived poses are almost invariant with regard to the observer, e.g., the brown stick points to the observer regardless of screen slant. 1 A, Center is viewed from different angles in Fig. The situation seems quite different when a picture of the 3D scene in Fig. 1 B, Left) to approximately orthogonal ( Fig. The perceived angle between the two bodies changes from obtuse ( Fig. 1 B shows two dolls lying on the ground, pictured from different camera positions. 1 A, Right), illustrating striking variations in perception of a fixed 3D scene across viewpoints. 1 A, Left) to approximately orthogonal ( Fig. The angle between the perceived poses of the two sticks changes from obtuse ( Fig. 1 A, show one pair of connected sticks lying on the ground, pictured from different camera positions. The inferential rules simply explain both perceptual invariance and dramatic distortions in poses of real and pictured objects, and show the benefits of incorporating projective geometry of light into mental inferences about 3D scenes. By considering changes in retinal image orientations due to position and elevation of limbs, the model also explains perceived limb poses in a complex scene of two bodies lying on the ground. We show that a model that uses the back-projection, modulated by just two free parameters, explains 560 pose estimates per observer. The inverted expression yields the back-projection for each object pose, camera elevation, and observer viewpoint. The projection of each 3D stick to the 2D picture, and then onto the retina, is described by an invertible trigonometric expression. Observers viewed these from five directions, and matched the perceived pose of each stick by rotating an arrow on a horizontal touchscreen. We used pictures of single sticks or pairs of joined sticks taken from different camera angles. Pose estimations are altered by a fronto-parallel bias, and by image distortions that appear to tilt the ground plane. We further show that all observers apply the same inferential rule from all viewpoints, utilizing the geometrically derived back-transform from retinal images to actual 3D scenes. We show unexpectedly remarkable agreement in the 3D poses different observers estimate from pictures. Pose estimation of objects in real scenes is critically important for biological and machine visual systems, but little is known of how humans infer 3D poses from 2D retinal images.
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