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We simulated two centralized methods and two decentralized methods for multi-robot safe navigation in their respective environments.
We trained a model to identify the image of staplers (distinguish from mechanical arms as an example). After image recognition, we analyzed the feature information contained in the obtained image to help design. This method can also use in other product designs.
POEM (Point Embedded Multi-view) focuses on reconstructing an articulation body with “true scale” and “accurate pose” from a series of sparsely arranged camera views. In practice, we used the example of hand. POEM explores the power of points, using a cluster of (x, y, z) coordinates with natural positional encoding to find associations in multi-view stereo.
We proposed a new method CHORD which exploits the categorical shape prior for reconstructing the shape of intra-class objects. In addition, we constructed a new dataset, COMIC, of category-level hand-object interaction. COMIC encompasses a diverse collection of object instances, materials, hand interactions, and viewing directions, as illustrated.
Color-NeuS focuses on mesh reconstruction with color. We remove view-dependent color while using a relighting network to maintain volume rendering performance. Mesh is extracted from the SDF network, and vertex color is derived from the global color network. We conceived a in hand object scanning task and gathered several videos for it to evaluate Color-NeuS.
Spring-Gaus reconstructs the appearance, geometry, and physical dynamics properties of elastic objects from video observations. Spring-Gaus enables future prediction and simulation under different initial states and environmental parameters.
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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