activity
20212026
most citedUnderstanding Human-AI Collaboration in Music Therapy Through Co-Design with Therapists

30 citations · 165 across the 52 of their papers we have counts for

collaborators
Showing cs.CVShow all

24 papers · 1 filter

cs.CV2024

Locate n' Rotate: Two-stage Openable Part Detection with Foundation Model Priors

Siqi Li, Xiaoxue Chen, Haoyu Cheng +3

Detecting the openable parts of articulated objects is crucial for downstream applications in intelligent robotics, such as pulling a drawer. This task poses a multitasking challen…

cs.CV2024

RGM: Reconstructing High-fidelity 3D Car Assets with Relightable 3D-GS Generative Model from a Single Image

Xiaoxue Chen, Jv Zheng, Hao Huang +8

The generation of high-quality 3D car assets is essential for various applications, including video games, autonomous driving, and virtual reality. Current 3D generation methods ut…

cs.CV2024

Active Neural Mapping at Scale

Zijia Kuang, Zike Yan, Hao Zhao +2

We introduce a NeRF-based active mapping system that enables efficient and robust exploration of large-scale indoor environments. The key to our approach is the extraction of a gen…

cs.CV2024

Camera Relocalization in Shadow-free Neural Radiance Fields

Shiyao Xu, Caiyun Liu, Yuantao Chen +5

Camera relocalization is a crucial problem in computer vision and robotics. Recent advancements in neural radiance fields (NeRFs) have shown promise in synthesizing photo-realistic…

cs.CV2024

Blending Distributed NeRFs with Tri-stage Robust Pose Optimization

Baijun Ye, Caiyun Liu, Xiaoyu Ye +6

Due to the limited model capacity, leveraging distributed Neural Radiance Fields (NeRFs) for modeling extensive urban environments has become a necessity. However, current distribu…

cs.CV2024★ 1 cited

Adaptive Surface Normal Constraint for Geometric Estimation from Monocular Images

Xiaoxiao Long, Yuhang Zheng, Yupeng Zheng +6

We introduce a novel approach to learn geometries such as depth and surface normal from images while incorporating geometric context. The difficulty of reliably capturing geometric…