38 citations · 72 across the 8 of their papers we have counts for
8 papers
SPAR3D: Stable Point-Aware Reconstruction of 3D Objects from Single Images
Zixuan Huang, Mark Boss, Aaryaman Vasishta +2
We study the problem of single-image 3D object reconstruction. Recent works have diverged into two directions: regression-based modeling and generative modeling. Regression methods…
Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models
Mateusz Michalkiewicz, Sheena Bai, Mahsa Baktashmotlagh +2
In this paper, we analyze the viewpoint stability of foundational models - specifically, their sensitivity to changes in viewpoint- and define instability as significant feature va…
ConDense: Consistent 2D/3D Pre-training for Dense and Sparse Features from Multi-View Images
Xiaoshuai Zhang, Zhicheng Wang, Howard Zhou +5
To advance the state of the art in the creation of 3D foundation models, this paper introduces the ConDense framework for 3D pre-training utilizing existing pre-trained 2D networks…
CPL: Counterfactual Prompt Learning for Vision and Language Models
Xuehai He, Diji Yang, Weixi Feng +7
Prompt tuning is a new few-shot transfer learning technique that only tunes the learnable prompt for pre-trained vision and language models such as CLIP. However, existing prompt t…
SAMURAI: Shape And Material from Unconstrained Real-world Arbitrary Image collections
Mark Boss, Andreas Engelhardt, Abhishek Kar +5
Inverse rendering of an object under entirely unknown capture conditions is a fundamental challenge in computer vision and graphics. Neural approaches such as NeRF have achieved ph…
Planes vs. Chairs: Category-guided 3D shape learning without any 3D cues
Zixuan Huang, Stefan Stojanov, Anh Thai +2
We present a novel 3D shape reconstruction method which learns to predict an implicit 3D shape representation from a single RGB image. Our approach uses a set of single-view images…