Search Results for author: Li Yi

Found 36 papers, 15 papers with code

Fixing Malfunctional Objects With Learned Physical Simulation and Functional Prediction

no code implementations5 May 2022 Yining Hong, Kaichun Mo, Li Yi, Leonidas J. Guibas, Antonio Torralba, Joshua B. Tenenbaum, Chuang Gan

Specifically, FixNet consists of a perception module to extract the structured representation from the 3D point cloud, a physical dynamics prediction module to simulate the results of interactions on 3D objects, and a functionality prediction module to evaluate the functionality and choose the correct fix.

Rotationally Equivariant 3D Object Detection

no code implementations28 Apr 2022 Hong-Xing Yu, Jiajun Wu, Li Yi

To incorporate object-level rotation equivariance into 3D object detectors, we need a mechanism to extract equivariant features with local object-level spatial support while being able to model cross-object context information.

3D Object Detection Autonomous Driving

Multi-Robot Active Mapping via Neural Bipartite Graph Matching

no code implementations30 Mar 2022 Kai Ye, Siyan Dong, Qingnan Fan, He Wang, Li Yi, Fei Xia, Jue Wang, Baoquan Chen

Previous approaches either choose the frontier as the goal position via a myopic solution that hinders the time efficiency, or maximize the long-term value via reinforcement learning to directly regress the goal position, but does not guarantee the complete map construction.

Graph Matching reinforcement-learning

CodedVTR: Codebook-based Sparse Voxel Transformer with Geometric Guidance

no code implementations18 Mar 2022 Tianchen Zhao, Niansong Zhang, Xuefei Ning, He Wang, Li Yi, Yu Wang

We propose CodedVTR (Codebook-based Voxel TRansformer), which improves data efficiency and generalization ability for 3D sparse voxel transformers.

3D Semantic Segmentation

AutoGPart: Intermediate Supervision Search for Generalizable 3D Part Segmentation

no code implementations13 Mar 2022 Xueyi Liu, Xiaomeng Xu, Anyi Rao, Chuang Gan, Li Yi

To solve the above issues, we propose AutoGPart, a generic method enabling training generalizable 3D part segmentation networks with the task prior considered.

3D Part Segmentation Domain Generalization

HOI4D: A 4D Egocentric Dataset for Category-Level Human-Object Interaction

no code implementations3 Mar 2022 Yunze Liu, Yun Liu, Che Jiang, Kangbo Lyu, Weikang Wan, Hao Shen, Boqiang Liang, Zhoujie Fu, He Wang, Li Yi

We present HOI4D, a large-scale 4D egocentric dataset with rich annotations, to catalyze the research of category-level human-object interaction.

Action Segmentation Frame +4

On Learning Contrastive Representations for Learning with Noisy Labels

no code implementations3 Mar 2022 Li Yi, Sheng Liu, Qi She, A. Ian McLeod, Boyu Wang

To address this issue, we focus on learning robust contrastive representations of data on which the classifier is hard to memorize the label noise under the CE loss.

Learning with noisy labels Representation Learning

PTR: A Benchmark for Part-based Conceptual, Relational, and Physical Reasoning

no code implementations NeurIPS 2021 Yining Hong, Li Yi, Joshua B. Tenenbaum, Antonio Torralba, Chuang Gan

A critical aspect of human visual perception is the ability to parse visual scenes into individual objects and further into object parts, forming part-whole hierarchies.

Instance Segmentation Semantic Segmentation +1

Leveraging SE(3) Equivariance for Self-Supervised Category-Level Object Pose Estimation

no code implementations NeurIPS 2021 Xiaolong Li, Yijia Weng, Li Yi, Leonidas Guibas, A. Lynn Abbott, Shuran Song, He Wang

Category-level object pose estimation aims to find 6D object poses of previously unseen object instances from known categories without access to object CAD models.

Frame Pose Estimation +1

Leveraging SE(3) Equivariance for Self-supervised Category-Level Object Pose Estimation from Point Clouds

no code implementations NeurIPS 2021 Xiaolong Li, Yijia Weng, Li Yi, Leonidas Guibas, A. Lynn Abbott, Shuran Song, He Wang

To reduce the huge amount of pose annotations needed for category-level learning, we propose for the first time a self-supervised learning framework to estimate category-level 6D object pose from single 3D point clouds.

Frame Pose Estimation +1

P4Contrast: Contrastive Learning with Pairs of Point-Pixel Pairs for RGB-D Scene Understanding

no code implementations24 Dec 2020 Yunze Liu, Li Yi, Shanghang Zhang, Qingnan Fan, Thomas Funkhouser, Hao Dong

Self-supervised representation learning is a critical problem in computer vision, as it provides a way to pretrain feature extractors on large unlabeled datasets that can be used as an initialization for more efficient and effective training on downstream tasks.

Contrastive Learning Representation Learning +1

Compositionally Generalizable 3D Structure Prediction

1 code implementation4 Dec 2020 Songfang Han, Jiayuan Gu, Kaichun Mo, Li Yi, Siyu Hu, Xuejin Chen, Hao Su

However, there remains a much more difficult and under-explored issue on how to generalize the learned skills over unseen object categories that have very different shape geometry distributions.

3D Shape Reconstruction Translation

Complete & Label: A Domain Adaptation Approach to Semantic Segmentation of LiDAR Point Clouds

no code implementations CVPR 2021 Li Yi, Boqing Gong, Thomas Funkhouser

We study an unsupervised domain adaptation problem for the semantic labeling of 3D point clouds, with a particular focus on domain discrepancies induced by different LiDAR sensors.

Semantic Segmentation Unsupervised Domain Adaptation

Rethinking Sampling in 3D Point Cloud Generative Adversarial Networks

no code implementations12 Jun 2020 He Wang, Zetian Jiang, Li Yi, Kaichun Mo, Hao Su, Leonidas J. Guibas

We further study how different evaluation metrics weigh the sampling pattern against the geometry and propose several perceptual metrics forming a sampling spectrum of metrics.

Curriculum DeepSDF

1 code implementation ECCV 2020 Yueqi Duan, Haidong Zhu, He Wang, Li Yi, Ram Nevatia, Leonidas J. Guibas

When learning to sketch, beginners start with simple and flexible shapes, and then gradually strive for more complex and accurate ones in the subsequent training sessions.

3D Shape Representation Representation Learning

SAPIEN: A SimulAted Part-based Interactive ENvironment

1 code implementation CVPR 2020 Fanbo Xiang, Yuzhe Qin, Kaichun Mo, Yikuan Xia, Hao Zhu, Fangchen Liu, Minghua Liu, Hanxiao Jiang, Yifu Yuan, He Wang, Li Yi, Angel X. Chang, Leonidas J. Guibas, Hao Su

To achieve this task, a simulated environment with physically realistic simulation, sufficient articulated objects, and transferability to the real robot is indispensable.

Category-Level Articulated Object Pose Estimation

2 code implementations CVPR 2020 Xiaolong Li, He Wang, Li Yi, Leonidas Guibas, A. Lynn Abbott, Shuran Song

We develop a deep network based on PointNet++ that predicts ANCSH from a single depth point cloud, including part segmentation, normalized coordinates, and joint parameters in the canonical object space.

Pose Estimation

StructEdit: Learning Structural Shape Variations

1 code implementation CVPR 2020 Kaichun Mo, Paul Guerrero, Li Yi, Hao Su, Peter Wonka, Niloy Mitra, Leonidas J. Guibas

Learning to encode differences in the geometry and (topological) structure of the shapes of ordinary objects is key to generating semantically plausible variations of a given shape, transferring edits from one shape to another, and many other applications in 3D content creation.

StructureNet: Hierarchical Graph Networks for 3D Shape Generation

2 code implementations1 Aug 2019 Kaichun Mo, Paul Guerrero, Li Yi, Hao Su, Peter Wonka, Niloy Mitra, Leonidas J. Guibas

We introduce StructureNet, a hierarchical graph network which (i) can directly encode shapes represented as such n-ary graphs; (ii) can be robustly trained on large and complex shape families; and (iii) can be used to generate a great diversity of realistic structured shape geometries.

3D Shape Generation

AdaCoSeg: Adaptive Shape Co-Segmentation with Group Consistency Loss

no code implementations CVPR 2020 Chenyang Zhu, Kai Xu, Siddhartha Chaudhuri, Li Yi, Leonidas Guibas, Hao Zhang

While the part prior network can be trained with noisy and inconsistently segmented shapes, the final output of AdaCoSeg is a consistent part labeling for the input set, with each shape segmented into up to (a user-specified) K parts.

Instance Segmentation Semantic Segmentation

TextureNet: Consistent Local Parametrizations for Learning from High-Resolution Signals on Meshes

1 code implementation CVPR 2019 Jingwei Huang, Haotian Zhang, Li Yi, Thomas Funkhouser, Matthias Nießner, Leonidas Guibas

We introduce, TextureNet, a neural network architecture designed to extract features from high-resolution signals associated with 3D surface meshes (e. g., color texture maps).

3D Semantic Segmentation

Supervised Fitting of Geometric Primitives to 3D Point Clouds

2 code implementations CVPR 2019 Lingxiao Li, Minhyuk Sung, Anastasia Dubrovina, Li Yi, Leonidas Guibas

Fitting geometric primitives to 3D point cloud data bridges a gap between low-level digitized 3D data and high-level structural information on the underlying 3D shapes.

Shape Representation Of 3D Point Clouds

Deep Part Induction from Articulated Object Pairs

1 code implementation19 Sep 2018 Li Yi, Haibin Huang, Difan Liu, Evangelos Kalogerakis, Hao Su, Leonidas Guibas

In this paper, we explore how the observation of different articulation states provides evidence for part structure and motion of 3D objects.

Beyond Holistic Object Recognition: Enriching Image Understanding with Part States

no code implementations CVPR 2018 Cewu Lu, Hao Su, Yongyi Lu, Li Yi, Chi-Keung Tang, Leonidas Guibas

Important high-level vision tasks such as human-object interaction, image captioning and robotic manipulation require rich semantic descriptions of objects at part level.

Human-Object Interaction Detection Image Captioning +1

SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation

no code implementations CVPR 2017 Li Yi, Hao Su, Xingwen Guo, Leonidas Guibas

To enable the prediction of vertex functions on them by convolutional neural networks, we resort to spectral CNN method that enables weight sharing by parameterizing kernels in the spectral domain spanned by graph laplacian eigenbases.

3D Part Segmentation

Dialogue Session Segmentation by Embedding-Enhanced TextTiling

no code implementations13 Oct 2016 Yiping Song, Lili Mou, Rui Yan, Li Yi, Zinan Zhu, Xiaohua Hu, Ming Zhang

In human-computer conversation systems, the context of a user-issued utterance is particularly important because it provides useful background information of the conversation.

Word Embeddings

Learning Discriminative Representations for Semantic Cross Media Retrieval

no code implementations18 Nov 2015 Jiang Aiwen, Li Hanxi, Li Yi, Wang Mingwen

As a result, an efficient linear semantic down mapping is jointly learned for multimodal data, leading to a common space where they can be compared.

Representation Learning

3D-Assisted Image Feature Synthesis for Novel Views of an Object

no code implementations26 Nov 2014 Hao Su, Fan Wang, Li Yi, Leonidas Guibas

In this paper, given a single input image of an object, we synthesize new features for other views of the same object.

Image Retrieval

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