Search Results for author:
Found 5 papers, 3 papers with code
Sparsity Invariant CNNs
In this paper, we consider convolutional neural networks operating on sparse inputs with an application to depth upsampling from sparse laser scan data.
Ranked #16 on
Depth Completion
on KITTI Depth Completion
Joint Graph Decomposition & Node Labeling: Problem, Algorithms, Applications
In order to find feasible solutions efficiently, we define two local search algorithms that converge monotonously to a local optimum, offering a feasible solution at any time.
DeMoN: Depth and Motion Network for Learning Monocular Stereo
In this paper we formulate structure from motion as a learning problem.
Joint Graph Decomposition and Node Labeling: Problem, Algorithms, Applications
In order to find feasible solutions efficiently, we define two local search algorithms that converge monotonously to a local optimum, offering a feasible solution at any time.
Pixel-level Encoding and Depth Layering for Instance-level Semantic Labeling
Recent approaches for instance-aware semantic labeling have augmented convolutional neural networks (CNNs) with complex multi-task architectures or computationally expensive graphical models.
