6 citations · 30 across the 10 of their papers we have counts for
8 papers · 1 filter
CLNeRF: Continual Learning Meets NeRF
Zhipeng Cai, Matthias Mueller
Novel view synthesis aims to render unseen views given a set of calibrated images. In practical applications, the coverage, appearance or geometry of the scene may change over time…
Metric3D: Towards Zero-shot Metric 3D Prediction from A Single Image
Wei Yin, Chi Zhang, Hao Chen +5
Reconstructing accurate 3D scenes from images is a long-standing vision task. Due to the ill-posedness of the single-image reconstruction problem, most well-established methods are…
Online Continual Learning Without the Storage Constraint
Ameya Prabhu, Zhipeng Cai, Puneet Dokania +3
Traditional online continual learning (OCL) research has primarily focused on mitigating catastrophic forgetting with fixed and limited storage allocation throughout an agent's lif…
SimCS: Simulation for Domain Incremental Online Continual Segmentation
Motasem Alfarra, Zhipeng Cai, Adel Bibi +2
Continual Learning is a step towards lifelong intelligence where models continuously learn from recently collected data without forgetting previous knowledge. Existing continual le…
Consensus Maximization Tree Search Revisited
Zhipeng Cai, Tat-Jun Chin, Vladlen Koltun
Consensus maximization is widely used for robust fitting in computer vision. However, solving it exactly, i.e., finding the globally optimal solution, is intractable. A* tree searc…
Practical optimal registration of terrestrial LiDAR scan pairs
Zhipeng Cai, Tat-Jun Chin, Alvaro Parra Bustos +1
Point cloud registration is a fundamental problem in 3D scanning. In this paper, we address the frequent special case of registering terrestrial LiDAR scans (or, more generally, le…
Deterministic consensus maximization with biconvex programming
Zhipeng Cai, Tat-Jun Chin, Huu Le +1
Consensus maximization is one of the most widely used robust fitting paradigms in computer vision, and the development of algorithms for consensus maximization is an active researc…
Robust Fitting in Computer Vision: Easy or Hard?
Tat-Jun Chin, Zhipeng Cai, Frank Neumann
Robust model fitting plays a vital role in computer vision, and research into algorithms for robust fitting continues to be active. Arguably the most popular paradigm for robust fi…