activity
20152025
most citedContextNet: Exploring Context and Detail for Semantic Segmentation in Real-time

200 citations · 201 across the 4 of their papers we have counts for

collaborators

6 papers

cs.CV2025

Best Foot Forward: Robust Foot Reconstruction in-the-wild

Kyle Fogarty, Jing Yang, Chayan Kumar Patodi +5

Accurate 3D foot reconstruction is crucial for personalized orthotics, digital healthcare, and virtual fittings. However, existing methods struggle with incomplete scans and anatom…

cs.CV2021

Self-Supervised Depth Completion for Active Stereo

Frederik Warburg, Daniel Hernandez-Juarez, Juan Tarrio +3

Active stereo systems are used in many robotic applications that require 3D information. These depth sensors, however, suffer from stereo artefacts and do not provide dense depth e…

cs.CV2020

Towards Bounding-Box Free Panoptic Segmentation

Ujwal Bonde, Pablo F. Alcantarilla, Stefan Leutenegger

In this work we introduce a new Bounding-Box Free Network (BBFNet) for panoptic segmentation. Panoptic segmentation is an ideal problem for proposal-free methods as it already requ…

cs.CV2018

Weakly supervised learning of indoor geometry by dual warping

Pulak Purkait, Ujwal Bonde, Christopher Zach

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. Indoor environments are part…

cs.CV2018★ 200 cited

ContextNet: Exploring Context and Detail for Semantic Segmentation in Real-time

Rudra P K Poudel, Ujwal Bonde, Stephan Liwicki +1

Modern deep learning architectures produce highly accurate results on many challenging semantic segmentation datasets. State-of-the-art methods are, however, not directly transfera…

cs.CV2015★ 1 cited

TemplateNet for Depth-Based Object Instance Recognition

Ujwal Bonde, Vijay Badrinarayanan, Roberto Cipolla +1

We present a novel deep architecture termed templateNet for depth based object instance recognition. Using an intermediate template layer we exploit prior knowledge of an object's…