arXiv Computer Vision
4d ago

Rethinking Asymmetry in Self-Supervised 3D Geometry Learning

The paper proposes a new self‑supervised approach for learning 3D geometry from unposed images by shifting the asymmetry from the input space to the 3D representation space. By excluding held‑out views only from the reconstructed scene while using a shared backbone for all views, the method improves depth and pose estimation. It further leverages view density differences and aligns features with a frozen vision foundation model to provide additional geometric training signals without requiring annotations.

By Gyeongjin Kang, Freeman Cheng, Botao Ye, Weijie Lyu, Ming-Hsuan Yang, Eunbyung Park
arXiv Computer Vision
Sep 7

Learning 3D Editing without Paired Supervision via Generative Prior Distillation

The paper introduces a framework for instruction‑guided 3D editing that does not require paired 3D supervision. It distills visual, semantic, and geometric knowledge from foundation models into a 3D editing model using a differentiable rendering pipeline, guided by a 2D visual prior from an image editing model and a semantic prior from a Vision‑Language Model. A 3D‑aware Distribution Matching regularization is added to prevent geometric collapse and ensure realistic 3D outputs, leading to superior instruction fidelity and cross‑view consistency compared to state‑of‑the‑art baselines.

By Hao Wen, Weibin Yun, Hongxing Fan, Haotian Lu, Rui Chen, Zehuan Huang, Lu Sheng
arXiv Machine Learning
Jul 14

TOLiD: Bridging the Architecture Gap in Vision Foundation Model to LiDAR Pretraining via Token Lifting for Distillation

arXiv:2607. 10762v1 Announce Type: cross Abstract: Cross-modal distillation from Vision Foundation Models (VFMs) to LiDAR backbones has recently emerged as a self-supervised pretraining strategy that reduces reliance on dense point-wise annotation for 3D scene understanding.

By Sutharsan Mahendran, Darshana Priyasad, Kaushik Roy, Tharindu Fernando, Sridha Sridharan, Clinton Fookes, Peyman Moghadam