Visual Intelligence Lab @ucsd_cse @UCSanDiego (Prof. Manmohan Chandraker and students)
San Diego
Joined April 2021
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Join us at #ICCV2023 in Paris for the inaugural workshop on Robust Computer Vision Across Geographies, which includes keynotes by an exciting line-up of speakers, and oral talks by the winners of the GeoNet domain adaptation challenge!
Website: geonet-challenge.github.io/I…
The test-set for the #GeoNet challenge has been released, and the submission portal for the challenge is now active!
Registration Link: forms.gle/zSZA1iaPD3mZxjyn7
Leaderboard: eval.ai/web/challenges/chall…
The deadline for the submission is September 5th.
#ICCV2023
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The GeoNet dataset will be presented at #CVPR2023 during the Wed PM poster session (#287), do drop by to know more about the efforts to improve geographical robustness of vision models @ViLabUCSD.
Paper Webpage: tarun005.github.io/GeoNet/
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Check out our new #CVPR2023 paper and dataset GeoNet, that explores challenges for robust and fair computer vision across geographies!
Webpage: tarun005.github.io/GeoNet/
PDF: arxiv.org/abs/2303.15443
Work done by @tarun_05, wangdong_xu and @manu_chandraker at @ucsd_cse.
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ViLab UCSD retweeted
Delighted to receive the Best Paper Honorable Mention @eccvconf #ECCV2022 for Neural Implicit Evolution, together with @ishit_mehta and Ravi Ramamoorthi @ucsd_cse @UCSanDiego @ViLabUCSD
Code and webpage: ishit.github.io/nie/
🗓️ 10/25 Oral at 1:30 PM (Hall E). Poster 115 at 3:30 (Hall B).
Physically-Based Editing of Indoor Scene Lighting from a Single Image.
Zhengqin Li, @manu_chandraker et. al.
Estimate visible and invisible light sources for indoor scene relighting !
bit.ly/3MY8TaB
🗓️ 10/27 Poster 27 at 11:00 AM (Hall B).
MemSAC: Memory Augmented Sample Consistency
for Large Scale Domain Adaptation
@tarun_05 @AstutiSharma @manu_chandraker
Cross-domain sample consistency approach to scale domain adaptation to large-scale datasets.
tarun005.github.io/MemSAC/
Check out our new work on level-set evolution of neural implicit surfaces. It allows energy formulations on triangle meshes to be used to deform neural implicits. A variety of explicit differentiable rendering and surface algorithms can now be used with free topological changes.
We anticipate potential applications in 3D reconstruction, inverse rendering, shape optimization, etc.
We will be at #ECCV2022 with an oral presentation. Project and code are at ishit.github.io/nie
Excited to share #PhotoScene at #CVPR2022 for creating photorealistic 3D digital twins from images! Chat with us (in-person!!) at poster session 4.1 during 10:00-12:30 on June 24 (Fri) and see our project page for more details!
Project page: yuyingyeh.github.io/projects…
Excited to invite you all to the oral talk (session 1.2, 1:30 PM, in-person!) of our paper #IRISFormer at #CVPR2022, where we propose a dense #transformer for single image inverse rendering.
Also find us at the poster session 1.2 to learn more about #IRISFormer and chat with us!
Check out #OpenRooms being released by @ViLabUCSD at #CVPR2021, an open framework for creating photorealistic counterparts of indoor 3D scans, with ground truth geometry, material, lighting, semantics and physical properties.
Check out the project page, the #CVPR2021 oral paper and other details here:
vilab-ucsd.github.io/ucsd-op….
Please visit our poster at #CVPR2021 on Wed, June 23, 2021 6:00– 8:30 EDT for more details. (PaperId: 10082).
Joint work by @LiZhengqin, Ting-Wei Yu, Shen Sang, Sarah Wang, Meng Song, Yuhan Liu, @YuYingYeh1, Rui Zhu, Nitesh Gundavarapu, Jia Shi, Sai Bi, Zexiang Xu, Hong-Xing Yu, Kalyan Sunkavalli, Miloš Hašan, Ravi Ramamoorthi, @manu_chandraker .