22 citations · 37 across the 12 of their papers we have counts for
8 papers · 1 filter
Learnable Chamfer Distance for Point Cloud Reconstruction
Tianxin Huang, Qingyao Liu, Xiangrui Zhao +2
As point clouds are 3D signals with permutation invariance, most existing works train their reconstruction networks by measuring shape differences with the average point-to-point d…
MaxQ: Multi-Axis Query for N:M Sparsity Network
Jingyang Xiang, Siqi Li, Junhao Chen +4
N:M sparsity has received increasing attention due to its remarkable performance and latency trade-off compared with structured and unstructured sparsity. However, existing N:M spa…
SSC-RS: Elevate LiDAR Semantic Scene Completion with Representation Separation and BEV Fusion
Jianbiao Mei, Yu Yang, Mengmeng Wang +3
Semantic scene completion (SSC) jointly predicts the semantics and geometry of the entire 3D scene, which plays an essential role in 3D scene understanding for autonomous driving s…
Multimodal-driven Talking Face Generation via a Unified Diffusion-based Generator
Chao Xu, Shaoting Zhu, Junwei Zhu +4
Multimodal-driven talking face generation refers to animating a portrait with the given pose, expression, and gaze transferred from the driving image and video, or estimated from t…
Rethinking Mobile Block for Efficient Attention-based Models
Jiangning Zhang, Xiangtai Li, Jian Li +7
This paper focuses on developing modern, efficient, lightweight models for dense predictions while trading off parameters, FLOPs, and performance. Inverted Residual Block (IRB) ser…
SuperLine3D: Self-supervised Line Segmentation and Description for LiDAR Point Cloud
Xiangrui Zhao, Sheng Yang, Tianxin Huang +4
Poles and building edges are frequently observable objects on urban roads, conveying reliable hints for various computer vision tasks. To repetitively extract them as features and…
Semantic Segmentation-assisted Scene Completion for LiDAR Point Clouds
Xuemeng Yang, Hao Zou, Xin Kong +5
Outdoor scene completion is a challenging issue in 3D scene understanding, which plays an important role in intelligent robotics and autonomous driving. Due to the sparsity of LiDA…
SSC: Semantic Scan Context for Large-Scale Place Recognition
Lin Li, Xin Kong, Xiangrui Zhao +2
Place recognition gives a SLAM system the ability to correct cumulative errors. Unlike images that contain rich texture features, point clouds are almost pure geometric information…