In three-dimensional (3-D) cluttered scenes such as foliage, deeper surfaces often are more shadowed and hence darker, and so depth and luminance often have negative covariance. We examined whether the sign of depth-luminance covariance plays a role in depth perception in 3-D clutter. We compared scenes rendered with negative and positive depth-luminance covariance where positive covariance means that deeper surfaces are brighter and negative covariance means deeper surfaces are darker. For each scene, the sign of the depth-luminance covariance was given by occlusion cues. We tested whether subjects could use this sign information to judge the depth order of two target surfaces embedded in 3-D clutter. The clutter consisted of distractor surfaces that were randomly distributed in a 3-D volume. We tested three independent variables: the sign of the depth-luminance covariance, the colors of the targets and distractors, and the background luminance. An analysis of variance showed two main effects: Subjects performed better when the deeper surfaces were darker and when the color of the target surfaces was the same as the color of the distractors. There was also a strong interaction: Subjects performed better under a negative depth-luminance covariance condition when targets and distractors had different colors than when they had the same color. Our results are consistent with a "dark means deep" rule, but the use of this rule depends on the similarity between the color of the targets and color of the 3-D clutter.

Abstract-This paper presents an adaptation of a vision and inertial-based state estimation algorithm for use in an underwater robot. The proposed approach combines information from an Inertial Measurement Unit (IMU) in the form of linear accelerations and angular velocities, depth data from a pressure sensor, and feature tracking from a monocular downward facing camera to estimate the 6DOF pose of the vehicle. To validate the approach, we present extensive experimental results from field trials conducted in underwater environments with varying lighting and visibility conditions, and we demonstrate successful application of the technique underwater.

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We examined how well human observers can discriminate the density of surfaces in two halves of a rotating three-dimensional cluttered sphere. The observer's task was to compare the density of the front versus back half or the left versus right half. We measured how the bias and sensitivity in judging the denser half depended on the level of occlusion and on the area and density of the surfaces in the clutter. When occlusion level was low, observers in the front-back task were biased to judge the back as denser, and when occlusion level was high they were biased to judge the front as denser. Weber fractions decreased as density increased for both the front-back and left-right tasks, consistent with previous findings for two-dimensional density discrimination. Weber fractions did not vary significantly with area for the front-back task, but increased with area for the left-right task, and we attribute this difference to occlusions that have different effects in the two tasks. We also ran model observers that compared the image occupancies of the two halves against a known expected difference. As the occlusion level increased, this expected difference followed a similar trend as the biases of the human observers, with a roughly constant offset between them. Weber fractions for human and model observers followed some similar trends, but there were discrepancies as well that can be partly explained by the information available to human versus model observers in carrying out their respective tasks.