"3d representation learning" Papers

14 papers found

Dynam3D: Dynamic Layered 3D Tokens Empower VLM for Vision-and-Language Navigation

Zihan Wang, Seungjun Lee, Gim Hee Lee

NEURIPS 2025oralarXiv:2505.11383
7
citations

Global-Aware Monocular Semantic Scene Completion with State Space Models

Shijie Li, Zhongyao Cheng, Rong Li et al.

ICCV 2025arXiv:2503.06569
1
citations

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning

Yiyang Chen, Shanshan Zhao, Lunhao Duan et al.

ICCV 2025arXiv:2507.09102

LaGeM: A Large Geometry Model for 3D Representation Learning and Diffusion

Biao Zhang, Peter Wonka

ICLR 2025arXiv:2410.01295
11
citations

MoST: Efficient Monarch Sparse Tuning for 3D Representation Learning

Xu Han, Yuan Tang, Jinfeng Xu et al.

CVPR 2025arXiv:2503.18368
2
citations

Nautilus: Locality-aware Autoencoder for Scalable Mesh Generation

Yuxuan Wang, Xuanyu Yi, Haohan Weng et al.

ICCV 2025arXiv:2501.14317
10
citations

Point-MaDi: Masked Autoencoding with Diffusion for Point Cloud Pre-training

Xiaoyang Xiao, Runzhao Yao, Zhiqiang Tian et al.

NEURIPS 2025

Progressive Rendering Distillation: Adapting Stable Diffusion for Instant Text-to-Mesh Generation without 3D Data

Zhiyuan Ma, Xinyue Liang, Rongyuan Wu et al.

CVPR 2025arXiv:2503.21694
2
citations

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views

Xiangdong Zhang, Shaofeng Zhang, Junchi Yan

ICCV 2025arXiv:2509.01250
2
citations

What We Miss Matters: Learning from the Overlooked in Point Cloud Transformers

Yi Wang, Jiaze Wang, Ziyu Guo et al.

NEURIPS 2025

GeoAuxNet: Towards Universal 3D Representation Learning for Multi-sensor Point Clouds

Shengjun Zhang, Xin Fei, Yueqi Duan

CVPR 2024arXiv:2403.19220
5
citations

Multi-modal Relation Distillation for Unified 3D Representation Learning

Huiqun Wang, Yiping Bao, Panwang Pan et al.

ECCV 2024arXiv:2407.14007
4
citations

NeRF-MAE: Masked AutoEncoders for Self-Supervised 3D Representation Learning for Neural Radiance Fields

Muhammad Zubair Irshad, Sergey Zakharov, Vitor Guizilini et al.

ECCV 2024arXiv:2404.01300
22
citations

Towards Compact 3D Representations via Point Feature Enhancement Masked Autoencoders

Yaohua Zha, Huizhen Ji, Jinmin Li et al.

AAAI 2024paperarXiv:2312.10726
61
citations