Adversarially masking synthetic to mimic real: Adaptive noise injection for point cloud segmentation adaptation
This paper considers the synthetic-to-real adaptation of point cloud semantic segmentation,
which aims to segment the real-world point clouds with only synthetic labels available …
which aims to segment the real-world point clouds with only synthetic labels available …
Dg-pic: Domain generalized point-in-context learning for point cloud understanding
Recent point cloud understanding research suffers from performance drops on unseen data,
due to the distribution shifts across different domains. While recent studies use Domain …
due to the distribution shifts across different domains. While recent studies use Domain …
Annotator: A generic active learning baseline for lidar semantic segmentation
Active learning, a label-efficient paradigm, empowers models to interactively query an oracle
for labeling new data. In the realm of LiDAR semantic segmentation, the challenges stem …
for labeling new data. In the realm of LiDAR semantic segmentation, the challenges stem …
Sira-pcr: Sim-to-real adaptation for 3d point cloud registration
Point cloud registration is essential for many applications. However, existing real datasets
require extremely tedious and costly annotations, yet may not provide accurate camera …
require extremely tedious and costly annotations, yet may not provide accurate camera …
Recent advances in multi-modal 3D scene understanding: A comprehensive survey and evaluation
Multi-modal 3D scene understanding has gained considerable attention due to its wide
applications in many areas, such as autonomous driving and human-computer interaction …
applications in many areas, such as autonomous driving and human-computer interaction …
A survey of label-efficient deep learning for 3D point clouds
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 …
learning. However, collecting large-scale precisely-annotated point clouds is extremely …
An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision
This review article comprehensively delves into the rapidly evolving field of domain
adaptation in computer and robotic vision. It offers a detailed technical analysis of the …
adaptation in computer and robotic vision. It offers a detailed technical analysis of the …
Can 3D Vision-Language Models Truly Understand Natural Language?
Rapid advancements in 3D vision-language (3D-VL) tasks have opened up new avenues
for human interaction with embodied agents or robots using natural language. Despite this …
for human interaction with embodied agents or robots using natural language. Despite this …
Self-supervised latent feature learning for partial point clouds recognition
Z Zhang, F Da - Pattern Recognition Letters, 2023 - Elsevier
Abstract 3D vision perception, especially point clouds classification is fundamental and
popular in safety-critical systems such as autonomous driving and robotics automation …
popular in safety-critical systems such as autonomous driving and robotics automation …
3D vision and language pretraining with large-scale synthetic data
3D Vision-Language Pre-training (3D-VLP) aims to provide a pre-train model which can
bridge 3D scenes with natural language, which is an important technique for embodied …
bridge 3D scenes with natural language, which is an important technique for embodied …