27,686 research outputs found
Source coding for transmission of reconstructed dynamic geometry: a rate-distortion-complexity analysis of different approaches
Live 3D reconstruction of a human as a 3D mesh with commodity electronics is becoming a reality. Immersive applications (i.e. cloud gaming, tele-presence) benefit from effective transmission of such content over a bandwidth limited link. In this paper we outline different approaches for compressing live reconstructed mesh geometry based on distributing mesh reconstruction functions between sender and receiver. We evaluate rate-performance-complexity of different configurations. First, we investigate 3D mesh compression methods (i.e. dynamic/static) from MPEG-4. Second, we evaluate the option of using octree based point cloud compression and receiver side surface reconstruction
3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks
We propose a method for reconstructing 3D shapes from 2D sketches in the form
of line drawings. Our method takes as input a single sketch, or multiple
sketches, and outputs a dense point cloud representing a 3D reconstruction of
the input sketch(es). The point cloud is then converted into a polygon mesh. At
the heart of our method lies a deep, encoder-decoder network. The encoder
converts the sketch into a compact representation encoding shape information.
The decoder converts this representation into depth and normal maps capturing
the underlying surface from several output viewpoints. The multi-view maps are
then consolidated into a 3D point cloud by solving an optimization problem that
fuses depth and normals across all viewpoints. Based on our experiments,
compared to other methods, such as volumetric networks, our architecture offers
several advantages, including more faithful reconstruction, higher output
surface resolution, better preservation of topology and shape structure.Comment: 3DV 2017 (oral
A Bayesian Approach to Manifold Topology Reconstruction
In this paper, we investigate the problem of statistical reconstruction of piecewise linear manifold topology. Given a noisy, probably undersampled point cloud from a one- or two-manifold, the algorithm reconstructs an approximated most likely mesh in a Bayesian sense from which the sample might have been taken. We incorporate statistical priors on the object geometry to improve the reconstruction quality if additional knowledge about the class of original shapes is available. The priors can be formulated analytically or learned from example geometry with known manifold tessellation. The statistical objective function is approximated by a linear programming / integer programming problem, for which a globally optimal solution is found. We apply the algorithm to a set of 2D and 3D reconstruction examples, demon-strating that a statistics-based manifold reconstruction is feasible, and still yields plausible results in situations where sampling conditions are violated
Point Scene Understanding via Disentangled Instance Mesh Reconstruction
Semantic scene reconstruction from point cloud is an essential and
challenging task for 3D scene understanding. This task requires not only to
recognize each instance in the scene, but also to recover their geometries
based on the partial observed point cloud. Existing methods usually attempt to
directly predict occupancy values of the complete object based on incomplete
point cloud proposals from a detection-based backbone. However, this framework
always fails to reconstruct high fidelity mesh due to the obstruction of
various detected false positive object proposals and the ambiguity of
incomplete point observations for learning occupancy values of complete
objects. To circumvent the hurdle, we propose a Disentangled Instance Mesh
Reconstruction (DIMR) framework for effective point scene understanding. A
segmentation-based backbone is applied to reduce false positive object
proposals, which further benefits our exploration on the relationship between
recognition and reconstruction. Based on the accurate proposals, we leverage a
mesh-aware latent code space to disentangle the processes of shape completion
and mesh generation, relieving the ambiguity caused by the incomplete point
observations. Furthermore, with access to the CAD model pool at test time, our
model can also be used to improve the reconstruction quality by performing mesh
retrieval without extra training. We thoroughly evaluate the reconstructed mesh
quality with multiple metrics, and demonstrate the superiority of our method on
the challenging ScanNet dataset
A Complete Method for Reconstructing an Elevation Surface of 3D Point Clouds
Reconstructing the surface of 3D point clouds is a reconstruction from a cloud of 3D points to a triangular mesh. This process approximates a discrete point cloud by a continuous/smooth surface depending on the input data and the applications of users. In this paper, we propose a complete method to reconstruct an elevation surface from 3D point clouds. The method consists of three steps. In the first step, we triangulate an elevation surface of 3D point cloud structured in a 3D grid. In the second step, we remove the outward triangles to deal with concave regions on the boundary of the triangular mesh. In the third step, we reconstruct this surface by filling the hole of triangular mesh. Our method could process very fast for triangulating the surface, preserve the topology and characteristic of the input surface after reconstruction
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