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Found 16 papers, 11 papers with code
PartSLIP++: Enhancing Low-Shot 3D Part Segmentation via Multi-View Instance Segmentation and Maximum Likelihood Estimation
Open-world 3D part segmentation is pivotal in diverse applications such as robotics and AR/VR.
One-2-3-45++: Fast Single Image to 3D Objects with Consistent Multi-View Generation and 3D Diffusion
Recent advancements in open-world 3D object generation have been remarkable, with image-to-3D methods offering superior fine-grained control over their text-to-3D counterparts.
Zero123++: a Single Image to Consistent Multi-view Diffusion Base Model
We report Zero123++, an image-conditioned diffusion model for generating 3D-consistent multi-view images from a single input view.
Distilling Large Vision-Language Model with Out-of-Distribution Generalizability
Model distillation, the process of creating smaller, faster models that maintain the performance of larger models, is a promising direction towards the solution.
One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization
Single image 3D reconstruction is an important but challenging task that requires extensive knowledge of our natural world.
OpenShape: Scaling Up 3D Shape Representation Towards Open-World Understanding
Due to their alignment with CLIP embeddings, our learned shape representations can also be integrated with off-the-shelf CLIP-based models for various applications, such as point cloud captioning and point cloud-conditioned image generation.
Ranked #5 on
Zero-Shot Transfer 3D Point Cloud Classification
on ModelNet40
(using extra training data)
PartSLIP: Low-Shot Part Segmentation for 3D Point Clouds via Pretrained Image-Language Models
Generalizable 3D part segmentation is important but challenging in vision and robotics.
LESS: Label-Efficient Semantic Segmentation for LiDAR Point Clouds
Our method co-designs an efficient labeling process with semi/weakly supervised learning and is applicable to nearly any 3D semantic segmentation backbones.
Frame Mining: a Free Lunch for Learning Robotic Manipulation from 3D Point Clouds
We study how choices of input point cloud coordinate frames impact learning of manipulation skills from 3D point clouds.
Approximate Convex Decomposition for 3D Meshes with Collision-Aware Concavity and Tree Search
Approximate convex decomposition aims to decompose a 3D shape into a set of almost convex components, whose convex hulls can then be used to represent the input shape.
DeepMetaHandles: Learning Deformation Meta-Handles of 3D Meshes with Biharmonic Coordinates
Given a collection of 3D meshes of a category and their deformation handles (control points), our method learns a set of meta-handles for each shape, which are represented as combinations of the given handles.
Meshing Point Clouds with Predicted Intrinsic-Extrinsic Ratio Guidance
We are interested in reconstructing the mesh representation of object surfaces from point clouds.
SAPIEN: A SimulAted Part-based Interactive ENvironment
To achieve this task, a simulated environment with physically realistic simulation, sufficient articulated objects, and transferability to the real robot is indispensable.
Morphing and Sampling Network for Dense Point Cloud Completion
3D point cloud completion, the task of inferring the complete geometric shape from a partial point cloud, has been attracting attention in the community.
Ranked #8 on
Point Cloud Completion
on ShapeNet
Multi-task Batch Reinforcement Learning with Metric Learning
To perform well, the policy must infer the task identity from collected transitions by modelling its dependency on states, actions and rewards.
Pre-training as Batch Meta Reinforcement Learning with tiMe
Combining ideas from Batch RL and Meta RL, we propose tiMe, which learns distillation of multiple value functions and MDP embeddings from only existing data.
