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Found 6 papers, 3 papers with code
Blur aware metric depth estimation with multi-focus plenoptic cameras
A method to calibrate the inverse model is then proposed.
Time to augment self-supervised visual representation learning
Specifically, our analyses reveal that: 1) 3-D object manipulations drastically improve the learning of object categories; 2) viewing objects against changing backgrounds is important for learning to discard background-related information from the latent representation.
Embodied vision for learning object representations
During each play session the agent views an object in multiple orientations before turning its body to view another object.
Leveraging blur information for plenoptic camera calibration
This paper presents a novel calibration algorithm for plenoptic cameras, especially the multi-focus configuration, where several types of micro-lenses are used, using raw images only.
Blur Aware Calibration of Multi-Focus Plenoptic Camera
This paper presents a novel calibration algorithm for Multi-Focus Plenoptic Cameras (MFPCs) using raw images only.
Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis from monocular image
In this paper, we present a novel approach, called Deep MANTA (Deep Many-Tasks), for many-task vehicle analysis from a given image.
Ranked #2 on
Vehicle Pose Estimation
on KITTI Cars Hard
(using extra training data)
