Search Results for author:
Found 8 papers, 5 papers with code
Point Cloud Matters: Rethinking the Impact of Different Observation Spaces on Robot Learning
In this study, we explore the influence of different observation spaces on robot learning, focusing on three predominant modalities: RGB, RGB-D, and point cloud.
PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm
In this paper, we introduce a novel universal 3D pre-training framework designed to facilitate the acquisition of efficient 3D representation, thereby establishing a pathway to 3D foundational models.
Ranked #1 on
3D Semantic Segmentation
on ScanNet++
(using extra training data)
UniPAD: A Universal Pre-training Paradigm for Autonomous Driving
In the context of autonomous driving, the significance of effective feature learning is widely acknowledged.
RH20T: A Comprehensive Robotic Dataset for Learning Diverse Skills in One-Shot
A key challenge in robotic manipulation in open domains is how to acquire diverse and generalizable skills for robots.
AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Time
Accurate whole-body multi-person pose estimation and tracking is an important yet challenging topic in computer vision.
X-NeRF: Explicit Neural Radiance Field for Multi-Scene 360$^{\circ} $ Insufficient RGB-D Views
In this paper, we focus on a rarely discussed but important setting: can we train one model that can represent multiple scenes, with 360$^\circ $ insufficient views and RGB-D images?
MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge
Autonomous agents have made great strides in specialist domains like Atari games and Go.
Multi-Person Full Body Pose Estimation
Multi-person pose estimation plays an important role in many fields.
