3d dimensions shape collage2/27/2023 ![]() ![]() As the properties of the materials are unknown at the beginning of construction, and because error propagation can hinder the efficacy of pre-planned assemblies with non-uniform components, the structure is planned on-the-fly: the desired position of each stone is computed immediately before it is placed, and any settling or unexpected deviations are accounted for. ![]() Cabin-mounted LiDAR sensors provide for terrain mapping, stone localization and digitization, and a planning algorithm determines the placement position of each stone. We introduce a process for constructing dry stone walls in situ, facilitated by a customized autonomous hydraulic excavator. On-site robotic construction not only has the potential to enable architectural assemblies that exceed the size and complexity practical with laboratory-based prefabrication methods, but also offers the opportunity to leverage context-specific, locally sourced materials that are inexpensive, abundant, and low in embodied energy. Extensive quantitative analysis shows that 3DStyleNet outperforms alternative data augmentation techniques for the downstream task of single-image 3D reconstruction. In addition, our method can serve as a valuable tool to create 3D data augmentations for computer vision tasks. We showcase our approach qualitatively on 3D content stylization, and provide user studies to validate the quality of our results. Given a small set of high-quality textured objects, our method can create many novel stylized shapes, resulting in effortless 3D content creation and style-ware data augmentation. Second, we jointly optimize our geometric style network and a pre-trained image style transfer network with losses defined over both the geometry and the rendering of the result. First, the geometric style network is trained on a large set of untextured 3D shapes. Our model, 3DStyleNet, is composed of two sub-networks trained in two stages. In addition, the texture style of the target is transferred to the warped source object with the help of a multi-view differentiable renderer. Given a pair of textured source and target objects, our method predicts a part-aware affine transformation field that naturally warps the source shape to imitate the overall geometric style of the target. We propose a method to create plausible geometric and texture style variations of 3D objects in the quest to democratize 3D content creation. We present an algorithm for 3D collage generation that serves as an artistic tool performing the challenging 3D processing tasks, thus enabling the artist to focus on the creative side of the process. ![]() Thus, this expressive but technically challenging artistic medium is a particularly good candidate to address using computer graphics methods. At the same time, it has also been acknowledged that for humans, the creation of compound 3D shapes is extremely taxing. The ability of such representations to convey multiple meanings has been recognized for centuries. In particular, we focus on 3D collage creation, namely, a generation of compound representations of objects. We achieve this through modification of the actual representation of 3D shapes rather than their images. In this work, we present a method aimed at harnessing this symbolic representation power to increase the expressiveness of the 3D models themselves. The ability of computer graphics to represent images symbolically has so far been used mostly to render existing models with greater clarity or with greater visual appeal. ![]()
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