activity
20152022
most citedMLCVNet: Multi-Level Context VoteNet for 3D Object Detection

28 citations · 114 across the 16 of their papers we have counts for

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Showing cs.CVShow all

21 papers · 1 filter

cs.CV20226 cited

3DRM:Pair-wise relation module for 3D object detection

Yuqing Lan, Yao Duan, Yifei Shi +2

Context has proven to be one of the most important factors in object layout reasoning for 3D scene understanding. Existing deep contextual models either learn holistic features for…

cs.CV20226 cited

Box2Seg: Learning Semantics of 3D Point Clouds with Box-Level Supervision

Yan Liu, Qingyong Hu, Yinjie Lei +3

Learning dense point-wise semantics from unstructured 3D point clouds with fewer labels, although a realistic problem, has been under-explored in literature. While existing weakly…

cs.CV2021

ROSEFusion: Random Optimization for Online Dense Reconstruction under Fast Camera Motion

Jiazhao Zhang, Chenyang Zhu, Lintao Zheng +1

Online reconstruction based on RGB-D sequences has thus far been restrained to relatively slow camera motions (<1m/s). Under very fast camera motion (e.g., 3m/s), the reconstructio…

cs.CV2021

Potential Convolution: Embedding Point Clouds into Potential Fields

Dengsheng Chen, Haowen Deng, Jun Li +3

Recently, various convolutions based on continuous or discrete kernels for point cloud processing have been widely studied, and achieve impressive performance in many applications,…

cs.CV20212 cited

StablePose: Learning 6D Object Poses from Geometrically Stable Patches

Yifei Shi, Junwen Huang, Xin Xu +2

We introduce the concept of geometric stability to the problem of 6D object pose estimation and propose to learn pose inference based on geometrically stable patches extracted from…

cs.CV20202 cited

Hausdorff Point Convolution with Geometric Priors

Pengdi Huang, Liqiang Lin, Fuyou Xue +3

Without a shape-aware response, it is hard to characterize the 3D geometry of a point cloud efficiently with a compact set of kernels. In this paper, we advocate the use of Hausdor…