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16 de ene. de 2019 · DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation. Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, Steven Lovegrove. Computer graphics, 3D computer vision and robotics communities have produced multiple approaches to representing 3D geometry for rendering and reconstruction.
@InProceedings{Park_2019_CVPR, author = {Park, Jeong Joon and Florence, Peter and Straub, Julian and Newcombe, Richard and Lovegrove, Steven}, title = {DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation}, booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year ...
16 de ene. de 2019 · This work introduces DeepSDF, a learned continuous Signed Distance Function (SDF) representation of a class of shapes that enables high quality shape representation, interpolation and completion from partial and noisy 3D input data.
1 de jun. de 2019 · Literature has shown several breakthroughs in deep learning for reconstruction of 3D 33 models from point clouds. Recently, the research community has seen great successes 34 in neural ...
Articles 1–20. Senior Research Scientist, Google DeepMind - Cited by 9,621 - Robotics - Artificial Intelligence - Computer Vision - Natural Language Processing.
In-spired by this, we split the experiment into two cases: 1) train a regular network without skip connections, 2) train a network by concatenating the input vector to every 4 layers (e.g. for 12 layer network the input vector will be concate-nated to the 4th, and 8th intermediate feature vectors).
Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, Steven Lovegrove; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 165-174. Abstract.
