3 citations · 7 across the 12 of their papers we have counts for
9 papers · 1 filter
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…
RSA: Resolving Scale Ambiguities in Monocular Depth Estimators through Language Descriptions
Ziyao Zeng, Yangchao Wu, Hyoungseob Park +6
We propose a method for metric-scale monocular depth estimation. Inferring depth from a single image is an ill-posed problem due to the loss of scale from perspective projection du…
UnCLe: Benchmarking Unsupervised Continual Learning for Depth Completion
Xien Chen, Rit Gangopadhyay, Michael Chu +3
We propose UnCLe, the first standardized benchmark for Unsupervised Continual Learning of a multimodal 3D reconstruction task: Depth completion aims to infer a dense depth map from…
NeuroBind: Towards Unified Multimodal Representations for Neural Signals
Fengyu Yang, Chao Feng, Daniel Wang +8
Understanding neural activity and information representation is crucial for advancing knowledge of brain function and cognition. Neural activity, measured through techniques like e…
WorDepth: Variational Language Prior for Monocular Depth Estimation
Ziyao Zeng, Daniel Wang, Fengyu Yang +6
Three-dimensional (3D) reconstruction from a single image is an ill-posed problem with inherent ambiguities, i.e. scale. Predicting a 3D scene from text description(s) is similarly…
All-day Depth Completion
Vadim Ezhov, Hyoungseob Park, Zhaoyang Zhang +7
We propose a method for depth estimation under different illumination conditions, i.e., day and night time. As photometry is uninformative in regions under low-illumination, we tac…
Test-Time Adaptation for Depth Completion
Hyoungseob Park, Anjali Gupta, Alex Wong
It is common to observe performance degradation when transferring models trained on some (source) datasets to target testing data due to a domain gap between them. Existing methods…
Binding Touch to Everything: Learning Unified Multimodal Tactile Representations
Fengyu Yang, Chao Feng, Ziyang Chen +8
The ability to associate touch with other modalities has huge implications for humans and computational systems. However, multimodal learning with touch remains challenging due to…