Point cloud data have been widely explored due to its superior accuracy and robustness
under various adverse situations. Meanwhile, deep neural networks (DNNs) have achieved …

Recent advancements in vision foundation models (VFMs) have opened up new
possibilities for versatile and efficient visual perception. In this work, we introduce Seal, a …

In contrast to numerous NLP and 2D computer vision foundational models, the learning of a
robust and highly generalized 3D foundational model poses considerably greater …

We propose a novel approach to self-supervised learning of point cloud representations by
differentiable neural rendering. Motivated by the fact that informative point cloud features …

Formula-driven supervised learning (FDSL) has been shown to be an effective method for
pre-training vision transformers, where ExFractalDB-21k was shown to exceed the pre …

Contrastive learning has recently demonstrated great potential for unsupervised pre-training
in 3D scene understanding tasks. However, most existing work randomly selects point …

Pre-training is a strong strategy for enhancing visual models to efficiently train them with a
limited number of labeled images. In semantic segmentation, creating annotation masks …

Point cloud data has been extensively studied due to its compact form and flexibility in
representing complex 3D structures. The ability of point cloud data to accurately capture and …

In this study, we introduce PlantSegNet, a novel neural network model for instance
segmentation of nearby objects with similar geometric structures. Our work addresses the …

Recent advancements in vision-language pre-training (eg, CLIP) have enabled 2D vision
models to benefit from language supervision. However, the joint representation learning of …