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Research Overview
3D Reconstruction
Depth Fusion using Successive Reprojections
3D Shape Completion
Deep Marching Cubes
Deep, Probabilistic and Semantic 3D Reconstruction
Learning Deep Representations of 3D
3D Datasets and Benchmarks
Sparsity Invariant CNNs
Efficient volumetric inference with OctNet
Motion Estimation and Scene Understanding
SphereNet
Unsupervised Learning of Flow with Occlusions
Slow Flow
Global Localization and Affordance Learning
Object Scene Flow
Deep Discrete Flow
Generative Models and Image Synthesis
Convergence and Stability of GAN training
Geometric Image Synthesis
Learning from Synthetic data
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