4 citations · 5 across the 3 of their papers we have counts for
6 papers · 1 filter
NoiseSDF2NoiseSDF: Learning Clean Neural Fields from Noisy Supervision
Tengkai Wang, Weihao Li, Ruikai Cui +2
Reconstructing accurate implicit surface representations from point clouds remains a challenging task, particularly when data is captured using low-quality scanning devices. These…
NumGrad-Pull: Numerical Gradient Guided Tri-plane Representation for Surface Reconstruction from Point Clouds
Ruikai Cui, Binzhu Xie, Shi Qiu +3
Reconstructing continuous surfaces from unoriented and unordered 3D points is a fundamental challenge in computer vision and graphics. Recent advancements address this problem by t…
Adaptive Low Rank Adaptation of Segment Anything to Salient Object Detection
Ruikai Cui, Siyuan He, Shi Qiu
Foundation models, such as OpenAI's GPT-3 and GPT-4, Meta's LLaMA, and Google's PaLM2, have revolutionized the field of artificial intelligence. A notable paradigm shift has been t…
Model Calibration in Dense Classification with Adaptive Label Perturbation
Jiawei Liu, Changkun Ye, Shan Wang +4
For safety-related applications, it is crucial to produce trustworthy deep neural networks whose prediction is associated with confidence that can represent the likelihood of corre…
P2C: Self-Supervised Point Cloud Completion from Single Partial Clouds
Ruikai Cui, Shi Qiu, Saeed Anwar +4
Point cloud completion aims to recover the complete shape based on a partial observation. Existing methods require either complete point clouds or multiple partial observations of…
Energy-Based Residual Latent Transport for Unsupervised Point Cloud Completion
Ruikai Cui, Shi Qiu, Saeed Anwar +2
Unsupervised point cloud completion aims to infer the whole geometry of a partial object observation without requiring partial-complete correspondence. Differing from existing dete…