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Source code for "Understanding Deep Image Representations by Inverting Them", CVPR 2015
TensorFlow implementation of "Learning from Simulated and Unsupervised Images through Adversarial Training"
Single/multi view image(s) to voxel reconstruction using a recurrent neural network
The 2D-3D-S dataset provides a variety of mutually registered modalities from 2D, 2.5D and 3D domains, with instance-level semantic and geometric annotations. It covers over 6,000 m2 collected in 6 large-scale indoor areas that originate from 3 different buildings. It contains over 70,000 RGB…
code for Holistically-Nested Edge Detection
The main features of s9xie/hed are: Computer Vision Applications, Edge Detection, Scene Understanding.
Projects with overlapping indexed features include: aravindhm/deep-goggle — Source code for "Understanding Deep Image Representations by Inverting Them", CVPR 2015. carpedm20/simulated-unsupervised-tensorflow — TensorFlow implementation of "Learning from Simulated and Unsupervised Images through Adversarial Training". chrischoy/3d-r2n2 — Single/multi view image(s) to voxel reconstruction using a recurrent neural network. cyang0515/noncuboidroom — Learning to Reconstruct 3D Non-Cuboid Room Layout from a Single RGB Image. drprojects/deepviewagg — Official implementation for Learning Multi-View Aggregation In the Wild for Large-Scale 3D Semantic Segmentation… alexsax/2d-3d-semantics — The 2D-3D-S dataset provides a variety of mutually registered modalities from 2D, 2.5D and 3D domains, with…