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Found 5 papers, 2 papers with code
Self-Supervised Depth Completion for Active Stereo
Active stereo systems are used in many robotic applications that require 3D information.
Towards Bounding-Box Free Panoptic Segmentation
Our approach is distinct from previous works in panoptic segmentation that rely on a combination of a semantic segmentation network with a computationally costly instance segmentation network based on bounding box proposals, such as Mask R-CNN, to guide the prediction of instance labels using a Mixture-of-Expert (MoE) approach.
Weakly supervised learning of indoor geometry by dual warping
A major element of depth perception and 3D understanding is the ability to predict the 3D layout of a scene and its contained objects for a novel pose.
ContextNet: Exploring Context and Detail for Semantic Segmentation in Real-time
Modern deep learning architectures produce highly accurate results on many challenging semantic segmentation datasets.
Ranked #79 on
Semantic Segmentation
on Cityscapes val
TemplateNet for Depth-Based Object Instance Recognition
We present a novel deep architecture termed templateNet for depth based object instance recognition.
