Offsetting-based hollowing is a solid modeling operation widely used in 3D printing, which can change the model's physical properties and reduce the weight by generating voids inside a model. However, a hollowing operation can lead to additional supporting structures for fabrication in interior voids, which cannot be removed. As a consequence, the result of a hollowing operation is affected by these additional supporting structures when applying the operation to optimize physical properties of different models. This paper proposes a support-free hollowing framework to overcome the difficulty of fabricating voids inside a solid. The challenge of computing a support-free hollowing is decomposed into a sequence of shape optimization steps, which are repeatedly applied to interior mesh surfaces. The optimization of physical properties in different applications can be easily integrated into our framework. Comparing to prior approaches that can generate support-free inner structures, our hollowing operation can reduce more volume of material and thus provide a larger solution space for physical optimization. Experimental tests are taken on a number of 3D models to demonstrate the effectiveness of this framework.
In the field of visual tracking, the methods of Discriminative Correlation Filters (DCF) have showed excellent performance, which rely heavily on the choice of feature descriptors. The Continuous Convolution Operator Tracker (C-COT) is a novel correlation filter to track the target position in the continuous domain, which achieved significant effects. However, as for various visual scenes, different feature descriptors are suitable to different environments. If each feature channel is given the same confidence during the tracking phase, it would limit the performance of some good features. To address this problem, this paper proposes an improved C-COT algorithm that can adaptively perform feature channel weighting. The Average Peak Correlation Energy (APCE) is used to evaluate the corresponding response map of each feature channel, guiding the target appearance model to give different weights to different features. Then, we can obtain the final weighted feature response map whose peak value is applied to locate the target. In addition, the C-COT updates the appearance model rigorously in every frame, which may lead to over-fitting and increase computional complexity. Therefore, in order to reduce the redundancy of the online training sample and avoid similar background interference, we adopts the method of Peak Side Lobe Ratio (PSLR) to update the model. We perform comprehensive experiments on OTB50 and OTB100. The results show that the improved tracker achieves better accuracy, especially in some specific video scenes. In addition, speed has also improved.
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