We introduce the problem of computing a human-perceived softness measure for virtual 3D objects. As the virtual objects do not exist in the real world, we do not directly consider their physical properties but instead compute the human-perceived softness of the geometric shapes. In an initial experiment, we find that humans are highly consistent in their responses when given a pair of vertices on a 3D model and asked to select the vertex that they perceive to be more soft. This motivates us to take a crowdsourcing and machine learning framework. We collect crowdsourced data for such pairs of vertices. We then combine a learning-to-rank approach and a multi-layer neural network to learn a non-linear softness measure mapping any vertex to a softness value. For a new 3D shape, we can use the learned measure to compute the relative softness of every vertex on its surface. We demonstrate the robustness of our framework with a variety of 3D shapes and compare our non-linear learning approach with a linear method from previous work. Finally, we demonstrate the accuracy of our learned measure with user studies comparing our measure with the human-perceived softness of both virtual and real objects, and we show the usefulness of our measure with some applications.

We introduce the problem of computing a human-perceived softness measure for virtual 3D objects. As the virtual objects do not exist in the real world, we do not directly consider their physical properties but instead compute the human-perceived softness of the geometric shapes. We collect crowdsourced data where humans rank their perception of the softness of vertex pairs on virtual 3D models. We then compute shape descriptors and use a learning-to-rank approach to learn a softness measure mapping any vertex to a softness value. Finally, we demonstrate our framework with a variety of 3D shapes.

While the concept of visual saliency has been previously explored in the areas of mesh and image processing, saliency detection also applies to other sensory stimuli. In this paper, we explore the problem of tactile mesh saliency, where we define salient points on a virtual mesh as those that a human is more likely to grasp, press, or touch if the mesh were a real-world object. We solve the problem of taking as input a 3D mesh and computing the relative tactile saliency of every mesh vertex. Since it is difficult to manually define a tactile saliency measure, we introduce a crowdsourcing and learning framework. It is typically easy for humans to provide relative rankings of saliency between vertices rather than absolute values. We thereby collect crowdsourced data of such relative rankings and take a learning-to-rank approach. We develop a new formulation to combine deep learning and learning-to-rank methods to compute a tactile saliency measure. We demonstrate our framework with a variety of 3D meshes and various applications including material suggestion for rendering and fabrication.

We propose a new framework to detect structural defects and characterize the variability in the electrical resistance of through-silicon vias (TSVs) in 3D ICs. Our method offers a number of advantages that have been hard to achieve in the past. In particular, the proposed framework provides high throughput TSV testing at pre-bonding stage. A resistive liquid electrode is placed at the back side of the device to conduct electric current from TSVs. The current passing through TSVs leads to heat generation which can be captured by a remote, high-sensitivity thermal camera. The captured thermal signatures from the TSVs are then contrasted against reference thermal maps generated from known good die and/or electro-thermal simulations of models of good TSVs. A proposed automatic classification technique is capable of determining the status of TSVs based on their thermal signatures. We demonstrate the viability of the proposed technique using extensive simulation results on realistic TSV configurations.

We propose new techniques for post-silicon power mapping and modeling of multi-core processors using infrared imaging and performance counter measurements. An accurate finite-element modeling framework is used to capture the relationship between temperature and power, while compensating for the artifacts introduced from substituting traditional heat removal mechanisms with oil-based infrared-transparent cooling mechanisms. We use thermal conditioning techniques to build leakage power models for the die. Utilizing the power maps identified from infrared mapping, we develop empirical power models for different processor blocks based on the measurements from the performance monitoring counters (PMCs), and utilize the PMC-based models to analyze the transient power consumption. In our experiments, we capture thermal images from a quad-core processor under different workload conditions, and then we reconstruct the dynamic and leakage power maps for different blocks. Our results show good accuracy in mapping and modeling, revealing good insights into the trends of power consumption in multi-core processors.