198 citations · 419 across the 13 of their papers we have counts for
16 papers
Neural Control Variates with Automatic Integration
Zilu Li, Guandao Yang, Qingqing Zhao +4
This paper presents a method to leverage arbitrary neural network architecture for control variates. Control variates are crucial in reducing the variance of Monte Carlo integratio…
ObjectCarver: Semi-automatic segmentation, reconstruction and separation of 3D objects
Gemmechu Hassena, Jonathan Moon, Ryan Fujii +4
Implicit neural fields have made remarkable progress in reconstructing 3D surfaces from multiple images; however, they encounter challenges when it comes to separating individual o…
Better Monocular 3D Detectors with LiDAR from the Past
Yurong You, Cheng Perng Phoo, Carlos Andres Diaz-Ruiz +5
Accurate 3D object detection is crucial to autonomous driving. Though LiDAR-based detectors have achieved impressive performance, the high cost of LiDAR sensors precludes their wid…
Reward Finetuning for Faster and More Accurate Unsupervised Object Discovery
Katie Z Luo, Zhenzhen Liu, Xiangyu Chen +7
Recent advances in machine learning have shown that Reinforcement Learning from Human Feedback (RLHF) can improve machine learning models and align them with human preferences. Alt…
Dynamo-Depth: Fixing Unsupervised Depth Estimation for Dynamical Scenes
Yihong Sun, Bharath Hariharan
Unsupervised monocular depth estimation techniques have demonstrated encouraging results but typically assume that the scene is static. These techniques suffer when trained on dyna…
Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features
Travis Zhang, Katie Luo, Cheng Perng Phoo +5
The rapid development of 3D object detection systems for self-driving cars has significantly improved accuracy. However, these systems struggle to generalize across diverse driving…