18 citations · 45 across the 14 of their papers we have counts for
5 papers · 1 filter
LoRA3D: Low-Rank Self-Calibration of 3D Geometric Foundation Models
Ziqi Lu, Heng Yang, Danfei Xu +4
Emerging 3D geometric foundation models, such as DUSt3R, offer a promising approach for in-the-wild 3D vision tasks. However, due to the high-dimensional nature of the problem spac…
Large Spatial Model: End-to-end Unposed Images to Semantic 3D
Zhiwen Fan, Jian Zhang, Wenyan Cong +10
Reconstructing and understanding 3D structures from a limited number of images is a well-established problem in computer vision. Traditional methods usually break this task into mu…
Neural Visibility Field for Uncertainty-Driven Active Mapping
Shangjie Xue, Jesse Dill, Pranay Mathur +3
This paper presents Neural Visibility Field (NVF), a novel uncertainty quantification method for Neural Radiance Fields (NeRF) applied to active mapping. Our key insight is that re…
EmerNeRF: Emergent Spatial-Temporal Scene Decomposition via Self-Supervision
Jiawei Yang, Boris Ivanovic, Or Litany +8
We present EmerNeRF, a simple yet powerful approach for learning spatial-temporal representations of dynamic driving scenes. Grounded in neural fields, EmerNeRF simultaneously capt…
Model-Driven Feed-Forward Prediction for Manipulation of Deformable Objects
Yinxiao Li, Yan Wang, Yonghao Yue +5
Robotic manipulation of deformable objects is a difficult problem especially because of the complexity of the many different ways an object can deform. Searching such a high dimens…