4 citations · 8 across the 5 of their papers we have counts for
13 papers
MeshXL: Neural Coordinate Field for Generative 3D Foundation Models
Sijin Chen, Xin Chen, Anqi Pang +11
The polygon mesh representation of 3D data exhibits great flexibility, fast rendering speed, and storage efficiency, which is widely preferred in various applications. However, giv…
Unicron: Economizing Self-Healing LLM Training at Scale
Tao He, Xue Li, Zhibin Wang +4
Training large-scale language models is increasingly critical in various domains, but it is hindered by frequent failures, leading to significant time and economic costs. Current f…
Data Pruning via Moving-one-Sample-out
Haoru Tan, Sitong Wu, Fei Du +4
In this paper, we propose a novel data-pruning approach called moving-one-sample-out (MoSo), which aims to identify and remove the least informative samples from the training set.…
Robust Geometry-Preserving Depth Estimation Using Differentiable Rendering
Chi Zhang, Wei Yin, Gang Yu +5
In this study, we address the challenge of 3D scene structure recovery from monocular depth estimation. While traditional depth estimation methods leverage labeled datasets to dire…
ICPC: Instance-Conditioned Prompting with Contrastive Learning for Semantic Segmentation
Chaohui Yu, Qiang Zhou, Zhibin Wang +1
Modern supervised semantic segmentation methods are usually finetuned based on the supervised or self-supervised models pre-trained on ImageNet. Recent work shows that transferring…
ES-MVSNet: Efficient Framework for End-to-end Self-supervised Multi-View Stereo
Qiang Zhou, Chaohui Yu, Jingliang Li +3
Compared to the multi-stage self-supervised multi-view stereo (MVS) method, the end-to-end (E2E) approach has received more attention due to its concise and efficient training pipe…