12 citations · 31 across the 12 of their papers we have counts for
12 papers
NeRF-Det: Learning Geometry-Aware Volumetric Representation for Multi-View 3D Object Detection
Chenfeng Xu, Bichen Wu, Ji Hou +8
We present NeRF-Det, a novel method for indoor 3D detection with posed RGB images as input. Unlike existing indoor 3D detection methods that struggle to model scene geometry, our m…
A Practical Stereo Depth System for Smart Glasses
Jialiang Wang, Daniel Scharstein, Akash Bapat +11
We present the design of a productionized end-to-end stereo depth sensing system that does pre-processing, online stereo rectification, and stereo depth estimation with a fallback…
Consistent Direct Time-of-Flight Video Depth Super-Resolution
Zhanghao Sun, Wei Ye, Jinhui Xiong +4
Direct time-of-flight (dToF) sensors are promising for next-generation on-device 3D sensing. However, limited by manufacturing capabilities in a compact module, the dToF data has a…
A Practical Second-order Latent Factor Model via Distributed Particle Swarm Optimization
Jialiang Wang, Yurong Zhong, Weiling Li
Latent Factor (LF) models are effective in representing high-dimension and sparse (HiDS) data via low-rank matrices approximation. Hessian-free (HF) optimization is an efficient me…
UMSNet: An Universal Multi-sensor Network for Human Activity Recognition
Jialiang Wang, Haotian Wei, Yi Wang +2
Human activity recognition (HAR) based on multimodal sensors has become a rapidly growing branch of biometric recognition and artificial intelligence. However, how to fully mine mu…
Toward Practical Monocular Indoor Depth Estimation
Cho-Ying Wu, Jialiang Wang, Michael Hall +2
The majority of prior monocular depth estimation methods without groundtruth depth guidance focus on driving scenarios. We show that such methods generalize poorly to unseen comple…
FBNetV5: Neural Architecture Search for Multiple Tasks in One Run
Bichen Wu, Chaojian Li, Hang Zhang +6
Neural Architecture Search (NAS) has been widely adopted to design accurate and efficient image classification models. However, applying NAS to a new computer vision task still req…
Level Set Binocular Stereo with Occlusions
Jialiang Wang, Todd Zickler
Localizing stereo boundaries and predicting nearby disparities are difficult because stereo boundaries induce occluded regions where matching cues are absent. Most modern computer…
Interpreting Robust Optimization via Adversarial Influence Functions
Zhun Deng, Cynthia Dwork, Jialiang Wang +1
Robust optimization has been widely used in nowadays data science, especially in adversarial training. However, little research has been done to quantify how robust optimization ch…
Improving Deep Stereo Network Generalization with Geometric Priors
Jialiang Wang, Varun Jampani, Deqing Sun +3
End-to-end deep learning methods have advanced stereo vision in recent years and obtained excellent results when the training and test data are similar. However, large datasets of…
Level Set Stereo for Cooperative Grouping with Occlusion
Jialiang Wang, Todd Zickler
Localizing stereo boundaries is difficult because matching cues are absent in the occluded regions that are adjacent to them. We introduce an energy and level-set optimizer that im…
Architecture Selection via the Trade-off Between Accuracy and Robustness
Zhun Deng, Cynthia Dwork, Jialiang Wang +1
We provide a general framework for characterizing the trade-off between accuracy and robustness in supervised learning. We propose a method and define quantities to characterize th…