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 …

We study the problem of 3D semantic segmentation from raw point clouds. Unlike existing
methods which primarily rely on a large amount of human annotations for training neural …

3D segmentation is a fundamental and challenging problem in computer vision with
applications in autonomous driving and robotics. It has received significant attention from the …

In the past decade, deep neural networks have achieved significant progress in point cloud
learning. However, collecting large-scale precisely-annotated point clouds is extremely …

Pseudo-labels are widely employed in weakly supervised 3D segmentation tasks where
only sparse ground-truth labels are available for learning. Existing methods often rely on …

Abstract We present a Multimodal Interlaced Transformer (MIT) that jointly considers 2D and
3D data for weakly supervised point cloud segmentation. Research studies have shown that …

With the popularity and advancement of 3D point cloud data acquisition technologies and
sensors, research into 3D point clouds has made considerable strides based on deep …

The goal of 3D pose transfer is to transfer the pose from the source mesh to the target mesh
while preserving the identity information (eg, face, body shape) of the target mesh. Deep …

Multi-Layer Perceptron (MLP) models are the bedrock of contemporary point cloud
processing. However, their complex network architectures obscure the source of their …

Sequential point clouds acquired by light detection and ranging (LiDAR) technology provide
accurate spatial information for environmental sensing. However, semantic segmentation of …