Search Results for author:
Found 23 papers, 10 papers with code
InterFusion: Text-Driven Generation of 3D Human-Object Interaction
In this study, we tackle the complex task of generating 3D human-object interactions (HOI) from textual descriptions in a zero-shot text-to-3D manner.
Learning Dual-arm Object Rearrangement for Cartesian Robots
To achieve the goal, the core idea is to develop an effective object-to-arm task assignment strategy for minimizing the cumulative task execution time and maximizing the dual-arm cooperation efficiency.
SAM-guided Graph Cut for 3D Instance Segmentation
Experimental results on the ScanNet, ScanNet++ and KITTI-360 datasets demonstrate that our method achieves robust segmentation performance and can generalize across different types of scenes.
Interaction-Driven Active 3D Reconstruction with Object Interiors
We introduce an active 3D reconstruction method which integrates visual perception, robot-object interaction, and 3D scanning to recover both the exterior and interior, i. e., unexposed, geometries of a target 3D object.
Neural Packing: from Visual Sensing to Reinforcement Learning
We present a novel learning framework to solve the transport-and-packing (TAP) problem in 3D.
AffordPose: A Large-scale Dataset of Hand-Object Interactions with Affordance-driven Hand Pose
We collect a total of 26. 7K hand-object interactions, each including the 3D object shape, the part-level affordance label, and the manually adjusted hand poses.
Semi-Weakly Supervised Object Kinematic Motion Prediction
First, although 3D dataset with fully annotated motion labels is limited, there are existing datasets and methods for object part semantic segmentation at large scale.
Self-Supervised Category-Level Articulated Object Pose Estimation with Part-Level SE(3) Equivariance
Category-level articulated object pose estimation aims to estimate a hierarchy of articulation-aware object poses of an unseen articulated object from a known category.
ARO-Net: Learning Implicit Fields from Anchored Radial Observations
We introduce anchored radial observations (ARO), a novel shape encoding for learning implicit field representation of 3D shapes that is category-agnostic and generalizable amid significant shape variations.
NIFT: Neural Interaction Field and Template for Object Manipulation
Given a few object manipulation demos, NIFT guides the generation of the interaction imitation for a new object instance by matching the Neural Interaction Template (NIT) extracted from the demos in the target Neural Interaction Field (NIF) defined for the new object.
Shape Completion with Points in the Shadow
The core challenge is to generate plausible geometries to fill the unobserved part of the object based on a partial scan, which is under-constrained and suffers from a huge solution space.
Active Self-Training for Weakly Supervised 3D Scene Semantic Segmentation
Since the preparation of labeled data for training semantic segmentation networks of point clouds is a time-consuming process, weakly supervised approaches have been introduced to learn from only a small fraction of data.
Photo-to-Shape Material Transfer for Diverse Structures
The image translation network translates the color from the exemplar to a projection of the 3D shape and the part segmentation from the projection to the exemplar.
Learning High-DOF Reaching-and-Grasping via Dynamic Representation of Gripper-Object Interaction
To resolve the sample efficiency issue in learning the high-dimensional and complex control of dexterous grasping, we propose an effective representation of grasping state characterizing the spatial interaction between the gripper and the target object.
Point cloud completion via structured feature maps using a feedback network
To efficiently exploit local structures and enhance point distribution uniformity, we propose IFNet, a point upsampling module with a self-correction mechanism that can progressively refine details of the generated dense point cloud.
Consistent Two-Flow Network for Tele-Registration of Point Clouds
In this paper, we present a learning-based technique that alleviates this problem, and allows registration between point clouds, presented in arbitrary poses, and having little or even no overlap, a setting that has been referred to as tele-registration.
Shape-driven Coordinate Ordering for Star Glyph Sets via Reinforcement Learning
Given a set of star glyphs associated to multiple class labels, we propose to use shape context descriptors to measure the perceptual distance between pairs of glyphs, and use the derived silhouette coefficient to measure the perception of class separability within the entire set.
TAP-Net: Transport-and-Pack using Reinforcement Learning
Using a learning-based approach, a trained network can learn and encode solution patterns to guide the solution of new problem instances instead of executing an expensive online search.
Predictive and Generative Neural Networks for Object Functionality
Given a 3D object in isolation, our functional similarity network (fSIM-NET), a variation of the triplet network, is trained to predict the functionality of the object by inferring functionality-revealing interaction contexts.
RPM-Net: Recurrent Prediction of Motion and Parts from Point Cloud
We show results of simultaneous motion and part predictions from synthetic and real scans of 3D objects exhibiting a variety of part mobilities, possibly involving multiple movable parts.
FAME: 3D Shape Generation via Functionality-Aware Model Evolution
Our tool supports constrained modeling, allowing users to restrict or steer the model evolution with functionality labels.
Graphics
Graph2Plan: Learning Floorplan Generation from Layout Graphs
The core component of our learning framework is a deep neural network, Graph2Plan, which converts a layout graph, along with a building boundary, into a floorplan that fulfills both the layout and boundary constraints.
Global-to-Local Generative Model for 3D Shapes
We also introduce new means to measure and evaluate the performance of an adversarial generative model.
