WebDec 12, 2024 · We provide networks that infer the space decomposition and local deep implicit functions from a 3D mesh or posed depth image. During experiments, we find that it provides 10.3 points higher surface reconstruction accuracy than the state-of-the-art (OccNet), while requiring fewer than 1 percent of the network parameters. Experiments … WebApr 8, 2024 · Implicit surface representations, such as signed-distance functions, combined with deep learning have led to impressive models which can represent detailed shapes of objects with arbitrary topology.
DIST: Rendering Deep Implicit Signed Distance Function With ...
WebDeep Implicit Surface Network (DISN) for predicting SDFs from single-view images (Figure 1). An SDF simply encodes the signed distance of each point sample in 3D from the boundary of the underlying shape. Thus, given a set of signed distance values, the shape can be extracted by identifying the iso-surface using methods such as Marching Cubes … WebMay 25, 2024 · The network is trained to predict and fill in missing data, and operates on an implicit surface representation that encodes both known and unknown space. This allows us to predict global structure ... red black and white tie dye
DISN: Deep Implicit Surface Network for High-quality …
WebReconstructing 3D shapes from single-view images has been a long-standing research problem. In this paper, we present DISN, a Deep Implicit Surface Net- work which can generate a high-quality detail-rich 3D mesh from a 2D image by predicting the underlying signed distance fields. In addition to utilizing global image features, DISN predicts the ... WebDec 14, 2024 · We are the first to introduce two implicit surface saliency network, ISSN and the one with contrastive saliency learning ISSN-CSL, to learn category-level shape saliency via deep implicit surface networks. To compare the smoothness and symmetry of saliency maps of different methods quantitatively, we introduce two evaluation metrics, … WebSep 3, 2024 · Similarly, Wang et al. introduced a deep implicit surface network (DISN) that predicts a symbolic distance function from a 2D image to represent a 3D surface. Given the predicted camera parameters, the points are projected onto a 2D plane to collect multi-scale features. Finally, DISN combines local features, global features and point features ... red black and white table settings