16 citations · 79 across the 61 of their papers we have counts for
20 papers · 1 filter
NAVCON: A Cognitively Inspired and Linguistically Grounded Corpus for Vision and Language Navigation
Karan Wanchoo, Xiaoye Zuo, Hannah Gonzalez +5
We present NAVCON, a large-scale annotated Vision-Language Navigation (VLN) corpus built on top of two popular datasets (R2R and RxR). The paper introduces four core, cognitively m…
HDGS: Textured 2D Gaussian Splatting for Enhanced Scene Rendering
Yunzhou Song, Heguang Lin, Jiahui Lei +2
Recent advancements in neural rendering, particularly 2D Gaussian Splatting (2DGS), have shown promising results for jointly reconstructing fine appearance and geometry by leveragi…
Continuous-Time Human Motion Field from Events
Ziyun Wang, Ruijun Zhang, Zi-Yan Liu +2
This paper addresses the challenges of estimating a continuous-time human motion field from a stream of events. Existing Human Mesh Recovery (HMR) methods rely predominantly on fra…
ETAP: Event-based Tracking of Any Point
Friedhelm Hamann, Daniel Gehrig, Filbert Febryanto +2
Tracking any point (TAP) recently shifted the motion estimation paradigm from focusing on individual salient points with local templates to tracking arbitrary points with global im…
Equivariant Ray Embeddings for Implicit Multi-View Depth Estimation
Yinshuang Xu, Dian Chen, Katherine Liu +4
Incorporating inductive bias by embedding geometric entities (such as rays) as input has proven successful in multi-view learning. However, the methods adopting this technique typi…
Neural decoding from stereotactic EEG: accounting for electrode variability across subjects
Georgios Mentzelopoulos, Evangelos Chatzipantazis, Ashwin G. Ramayya +5
Deep learning based neural decoding from stereotactic electroencephalography (sEEG) would likely benefit from scaling up both dataset and model size. To achieve this, combining dat…
Multimodal LLM Guided Exploration and Active Mapping using Fisher Information
Wen Jiang, Boshu Lei, Katrina Ashton +1
We present an active mapping system that plans for both long-horizon exploration goals and short-term actions using a 3D Gaussian Splatting (3DGS) representation. Existing methods…
Next Best Sense: Guiding Vision and Touch with FisherRF for 3D Gaussian Splatting
Matthew Strong, Boshu Lei, Aiden Swann +3
We propose a framework for active next best view and touch selection for robotic manipulators using 3D Gaussian Splatting (3DGS). 3DGS is emerging as a useful explicit 3D scene rep…
Recent Event Camera Innovations: A Survey
Bharatesh Chakravarthi, Aayush Atul Verma, Kostas Daniilidis +2
Event-based vision, inspired by the human visual system, offers transformative capabilities such as low latency, high dynamic range, and reduced power consumption. This paper prese…
BiEquiFormer: Bi-Equivariant Representations for Global Point Cloud Registration
Stefanos Pertigkiozoglou, Evangelos Chatzipantazis, Kostas Daniilidis
The goal of this paper is to address the problem of global point cloud registration (PCR) i.e., finding the optimal alignment between point clouds irrespective of the initial poses…
Improving Equivariant Model Training via Constraint Relaxation
Stefanos Pertigkiozoglou, Evangelos Chatzipantazis, Shubhendu Trivedi +1
Equivariant neural networks have been widely used in a variety of applications due to their ability to generalize well in tasks where the underlying data symmetries are known. Desp…
EqNIO: Subequivariant Neural Inertial Odometry
Royina Karegoudra Jayanth, Yinshuang Xu, Ziyun Wang +3
Neural networks are seeing rapid adoption in purely inertial odometry, where accelerometer and gyroscope measurements from commodity inertial measurement units (IMU) are used to re…
Motion-prior Contrast Maximization for Dense Continuous-Time Motion Estimation
Friedhelm Hamann, Ziyun Wang, Ioannis Asmanis +3
Current optical flow and point-tracking methods rely heavily on synthetic datasets. Event cameras are novel vision sensors with advantages in challenging visual conditions, but sta…
Many Perception Tasks are Highly Redundant Functions of their Input Data
Rahul Ramesh, Anthony Bisulco, Ronald W. DiTullio +4
We show that many perception tasks, from visual recognition, semantic segmentation, optical flow, depth estimation to vocalization discrimination, are highly redundant functions of…
MoSca: Dynamic Gaussian Fusion from Casual Videos via 4D Motion Scaffolds
Jiahui Lei, Yijia Weng, Adam Harley +2
We introduce 4D Motion Scaffolds (MoSca), a modern 4D reconstruction system designed to reconstruct and synthesize novel views of dynamic scenes from monocular videos captured casu…
Track Everything Everywhere Fast and Robustly
Yunzhou Song, Jiahui Lei, Ziyun Wang +2
We propose a novel test-time optimization approach for efficiently and robustly tracking any pixel at any time in a video. The latest state-of-the-art optimization-based tracking t…
Scalable Networked Feature Selection with Randomized Algorithm for Robot Navigation
Vivek Pandey, Arash Amini, Guangyi Liu +4
We address the problem of sparse selection of visual features for localizing a team of robots navigating an unknown environment, where robots can exchange relative position measure…
Uncertainty-Aware Deployment of Pre-trained Language-Conditioned Imitation Learning Policies
Bo Wu, Bruce D. Lee, Kostas Daniilidis +2
Large-scale robotic policies trained on data from diverse tasks and robotic platforms hold great promise for enabling general-purpose robots; however, reliable generalization to ne…
TRAM: Global Trajectory and Motion of 3D Humans from in-the-wild Videos
Yufu Wang, Ziyun Wang, Lingjie Liu +1
We propose TRAM, a two-stage method to reconstruct a human's global trajectory and motion from in-the-wild videos. TRAM robustifies SLAM to recover the camera motion in the presenc…
Beyond Uncertainty: Risk-Aware Active View Acquisition for Safe Robot Navigation and 3D Scene Understanding with FisherRF
Guangyi Liu, Wen Jiang, Boshu Lei +3
The active view acquisition problem has been extensively studied in the context of robot navigation using NeRF and 3D Gaussian Splatting. To enhance scene reconstruction efficiency…