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…

🗓️ 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/

🗓️ 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

🗓️ 10/25 Oral at 1:30 PM (Hall E). Poster #80 at 3:30 PM (Hall B). A Level Set Theory for Neural Implicit Evolution under Explicit Flows @ishit_mehta @manu_chandraker Ravi R. A level-set theory bridge between explicit and implicit shape representations. ishit.github.io/nie

The key insight is that gradients for energy minimization on explicit surfaces behave as explicit flow fields acting on the 0-surface of level-sets. This allows extensions to classical level-set theory to optimize for mesh-based energy functions with neural implicits.

Our theory of neural implicit evolution bridges an existing gap between Lagrangian shape representations like triangle meshes and Eulerian representations like parametric level-sets. It explains and improves recent works on differentiable rendering and iso-surface extraction.

We demonstrate applications such as inverse rendering with high-genus recovery, curvature-based deformations for surface smoothing and mean-curvature flow, and ARAP editing on neural implicits.

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.

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!

#OpenRooms also provides ground truth semantics and physical properties such as friction coefficients. Integration with physics engines such as PyBullet allows robotics applications like rearrangement.