2 citations · 3 across the 4 of their papers we have counts for
6 papers
Direct Object-Level Reconstruction via Probabilistic Gaussian Splatting
Shuai Guo, Ao Guo, Junchao Zhao +5
Object-level 3D reconstruction play important roles across domains such as cultural heritage digitization, industrial manufacturing, and virtual reality. However, existing Gaussian…
GO-N3RDet: Geometry Optimized NeRF-enhanced 3D Object Detector
Zechuan Li, Hongshan Yu, Yihao Ding +3
We propose GO-N3RDet, a scene-geometry optimized multi-view 3D object detector enhanced by neural radiance fields. The key to accurate 3D object detection is in effective voxel rep…
PointDiffuse: A Dual-Conditional Diffusion Model for Enhanced Point Cloud Semantic Segmentation
Yong He, Hongshan Yu, Mingtao Feng +5
Diffusion probabilistic models are traditionally used to generate colors at fixed pixel positions in 2D images. Building on this, we extend diffusion models to point cloud semantic…
Soft Masked Transformer for Point Cloud Processing with Skip Attention-Based Upsampling
Yong He, Hongshan Yu, Chaoxu Mu +5
Point cloud processing methods leverage local and global point features %at the feature level to cater to downstream tasks, yet they often overlook the task-level context inherent…
OST: Refining Text Knowledge with Optimal Spatio-Temporal Descriptor for General Video Recognition
Tongjia Chen, Hongshan Yu, Zhengeng Yang +3
Due to the resource-intensive nature of training vision-language models on expansive video data, a majority of studies have centered on adapting pre-trained image-language models t…
First Place Solution to the CVPR'2023 AQTC Challenge: A Function-Interaction Centric Approach with Spatiotemporal Visual-Language Alignment
Tom Tongjia Chen, Hongshan Yu, Zhengeng Yang +6
Affordance-Centric Question-driven Task Completion (AQTC) has been proposed to acquire knowledge from videos to furnish users with comprehensive and systematic instructions. Howeve…