4 papers
Triangular Consistency as a Universal Constraint for Learning Optical Flow
Yi Xiao, Carlos Rodriguez Coronel, Jing Zhan +3
We propose triangular consistency as a first-principled constraint for optical flow, which is agnostic to network architecture, supervision type, and dataset, and applies to both i…
Fisheye3R: Adapting Unified 3D Feed-Forward Foundation Models to Fisheye Lenses
Ruxiao Duan, Erin Hong, Dongxu Zhao +3
Feed-forward foundation models for multi-view 3-dimensional (3D) reconstruction have been trained on large-scale datasets of perspective images; when tested on wide field-of-view i…
CLAP: Unsupervised 3D Representation Learning for Fusion 3D Perception via Curvature Sampling and Prototype Learning
Runjian Chen, Hang Zhang, Avinash Ravichandran +4
Unsupervised 3D representation learning reduces the burden of labeling multimodal 3D data for fusion perception tasks. Among different pre-training paradigms, differentiable-render…
TREND: Unsupervised 3D Representation Learning via Temporal Forecasting for LiDAR Perception
Runjian Chen, Hyoungseob Park, Bo Zhang +3
Labeling LiDAR point clouds is notoriously time-and-energy-consuming, which spurs recent unsupervised 3D representation learning methods to alleviate the labeling burden in LiDAR p…