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20162023
most citedOpenShape: Scaling Up 3D Shape Representation Towards Open-World Understanding

39 citations · 50 across the 11 of their papers we have counts for

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7 papers · 1 filter

cs.CV2023

Towards Learning Geometric Eigen-Lengths Crucial for Fitting Tasks

Yijia Weng, Kaichun Mo, Ruoxi Shi +2

Some extremely low-dimensional yet crucial geometric eigen-lengths often determine the success of some geometric tasks. For example, the height of an object is important to measure…

cs.CV2023★ 39 cited

OpenShape: Scaling Up 3D Shape Representation Towards Open-World Understanding

Minghua Liu, Ruoxi Shi, Kaiming Kuang +6

We introduce OpenShape, a method for learning multi-modal joint representations of text, image, and point clouds. We adopt the commonly used multi-modal contrastive learning framew…

cs.CV2022

RendNet: Unified 2D/3D Recognizer With Latent Space Rendering

Ruoxi Shi, Xinyang Jiang, Caihua Shan +2

Vector graphics (VG) have been ubiquitous in our daily life with vast applications in engineering, architecture, designs, etc. The VG recognition process of most existing methods i…

cs.CV2022★ 2 cited

CPPF: Towards Robust Category-Level 9D Pose Estimation in the Wild

Yang You, Ruoxi Shi, Weiming Wang +1

In this paper, we tackle the problem of category-level 9D pose estimation in the wild, given a single RGB-D frame. Using supervised data of real-world 9D poses is tedious and erron…

cs.CV2021★ 1 cited

Skeleton Merger: an Unsupervised Aligned Keypoint Detector

Ruoxi Shi, Zhengrong Xue, Yang You +1

Detecting aligned 3D keypoints is essential under many scenarios such as object tracking, shape retrieval and robotics. However, it is generally hard to prepare a high-quality data…

cs.CV2021★ 4 cited

PRIN/SPRIN: On Extracting Point-wise Rotation Invariant Features

Yang You, Yujing Lou, Ruoxi Shi +5

Point cloud analysis without pose priors is very challenging in real applications, as the orientations of point clouds are often unknown. In this paper, we propose a brand new poin…

cs.CV2020

Keypoint Autoencoders: Learning Interest Points of Semantics

Ruoxi Shi, Zhengrong Xue, Xinyang Li

Understanding point clouds is of great importance. Many previous methods focus on detecting salient keypoints to identity structures of point clouds. However, existing methods negl…