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Found 34 papers, 17 papers with code
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Point cloud is an important type of geometric data structure.
Ranked #1 on
Semantic Segmentation
on S3DIS
(Number of params metric)
3D Semantic Segmentation
Few-Shot 3D Point Cloud Classification
+4
The AdobeIndoorNav Dataset: Towards Deep Reinforcement Learning based Real-world Indoor Robot Visual Navigation
Synthetic data suffers from domain gap to the real-world scenes while visual inputs rendered from 3D reconstructed scenes have undesired holes and artifacts.
Robotics
PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understanding
We present PartNet: a consistent, large-scale dataset of 3D objects annotated with fine-grained, instance-level, and hierarchical 3D part information.
Ranked #3 on
3D Instance Segmentation
on PartNet
StructureNet: Hierarchical Graph Networks for 3D Shape Generation
We introduce StructureNet, a hierarchical graph network which (i) can directly encode shapes represented as such n-ary graphs; (ii) can be robustly trained on large and complex shape families; and (iii) can be used to generate a great diversity of realistic structured shape geometries.
StructEdit: Learning Structural Shape Variations
Learning to encode differences in the geometry and (topological) structure of the shapes of ordinary objects is key to generating semantically plausible variations of a given shape, transferring edits from one shape to another, and many other applications in 3D content creation.
Learning to Group: A Bottom-Up Framework for 3D Part Discovery in Unseen Categories
We address the problem of discovering 3D parts for objects in unseen categories.
PT2PC: Learning to Generate 3D Point Cloud Shapes from Part Tree Conditions
3D generative shape modeling is a fundamental research area in computer vision and interactive computer graphics, with many real-world applications.
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.
Learning 3D Part Assembly from a Single Image
Autonomous assembly is a crucial capability for robots in many applications.
Rethinking Sampling in 3D Point Cloud Generative Adversarial Networks
We further study how different evaluation metrics weigh the sampling pattern against the geometry and propose several perceptual metrics forming a sampling spectrum of metrics.
Generative 3D Part Assembly via Dynamic Graph Learning
Analogous to buying an IKEA furniture, given a set of 3D parts that can assemble a single shape, an intelligent agent needs to perceive the 3D part geometry, reason to propose pose estimations for the input parts, and finally call robotic planning and control routines for actuation.
DSG-Net: Learning Disentangled Structure and Geometry for 3D Shape Generation
While significant progress has been made, especially with recent deep generative models, it remains a challenge to synthesize high-quality shapes with rich geometric details and complex structure, in a controllable manner.
Compositionally Generalizable 3D Structure Prediction
However, there remains a much more difficult and under-explored issue on how to generalize the learned skills over unseen object categories that have very different shape geometry distributions.
Where2Act: From Pixels to Actions for Articulated 3D Objects
One of the fundamental goals of visual perception is to allow agents to meaningfully interact with their environment.
VAT-Mart: Learning Visual Action Trajectory Proposals for Manipulating 3D ARTiculated Objects
In this paper, we propose object-centric actionable visual priors as a novel perception-interaction handshaking point that the perception system outputs more actionable guidance than kinematic structure estimation, by predicting dense geometry-aware, interaction-aware, and task-aware visual action affordance and trajectory proposals.
O2O-Afford: Annotation-Free Large-Scale Object-Object Affordance Learning
Contrary to the vast literature in modeling, perceiving, and understanding agent-object (e. g., human-object, hand-object, robot-object) interaction in computer vision and robotics, very few past works have studied the task of object-object interaction, which also plays an important role in robotic manipulation and planning tasks.
Learning to Regrasp by Learning to Place
In this paper, we explore whether a robot can learn to regrasp a diverse set of objects to achieve various desired grasp poses.
AdaAfford: Learning to Adapt Manipulation Affordance for 3D Articulated Objects via Few-shot Interactions
Perceiving and interacting with 3D articulated objects, such as cabinets, doors, and faucets, pose particular challenges for future home-assistant robots performing daily tasks in human environments.
IFR-Explore: Learning Inter-object Functional Relationships in 3D Indoor Scenes
While most works focus on single-object or agent-object visual functionality and affordances, our work proposes to study a new kind of visual relationship that is also important to perceive and model -- inter-object functional relationships (e. g., a switch on the wall turns on or off the light, a remote control operates the TV).
Object Pursuit: Building a Space of Objects via Discriminative Weight Generation
We perform an extensive study of the key features of the proposed framework and analyze the characteristics of the learned representations.
RoboAssembly: Learning Generalizable Furniture Assembly Policy in a Novel Multi-robot Contact-rich Simulation Environment
Part assembly is a typical but challenging task in robotics, where robots assemble a set of individual parts into a complete shape.
GIMO: Gaze-Informed Human Motion Prediction in Context
We perform an extensive study of the benefits of leveraging the eye gaze for ego-centric human motion prediction with various state-of-the-art architectures.
Fixing Malfunctional Objects With Learned Physical Simulation and Functional Prediction
Specifically, FixNet consists of a perception module to extract the structured representation from the 3D point cloud, a physical dynamics prediction module to simulate the results of interactions on 3D objects, and a functionality prediction module to evaluate the functionality and choose the correct fix.
DualAfford: Learning Collaborative Visual Affordance for Dual-gripper Manipulation
It is essential yet challenging for future home-assistant robots to understand and manipulate diverse 3D objects in daily human environments.
COPILOT: Human-Environment Collision Prediction and Localization from Egocentric Videos
In this work, we introduce the challenging problem of predicting collisions in diverse environments from multi-view egocentric videos captured from body-mounted cameras.
Seg&Struct: The Interplay Between Part Segmentation and Structure Inference for 3D Shape Parsing
We propose Seg&Struct, a supervised learning framework leveraging the interplay between part segmentation and structure inference and demonstrating their synergy in an integrated framework.
Summarize the Past to Predict the Future: Natural Language Descriptions of Context Boost Multimodal Object Interaction Anticipation
This task requires an understanding of the spatio-temporal context formed by past actions on objects, coined action context.
SceneHGN: Hierarchical Graph Networks for 3D Indoor Scene Generation with Fine-Grained Geometry
We propose SCENEHGN, a hierarchical graph network for 3D indoor scenes that takes into account the full hierarchy from the room level to the object level, then finally to the object part level.
Category-Level Multi-Part Multi-Joint 3D Shape Assembly
A successful joint-optimized assembly needs to satisfy the bilateral objectives of shape structure and joint alignment.
JacobiNeRF: NeRF Shaping with Mutual Information Gradients
We propose a method that trains a neural radiance field (NeRF) to encode not only the appearance of the scene but also semantic correlations between scene points, regions, or entities -- aiming to capture their mutual co-variation patterns.
Where2Explore: Few-shot Affordance Learning for Unseen Novel Categories of Articulated Objects
Our framework explicitly estimates the geometric similarity across different categories, identifying local areas that differ from shapes in the training categories for efficient exploration while concurrently transferring affordance knowledge to similar parts of the objects.
STOW: Discrete-Frame Segmentation and Tracking of Unseen Objects for Warehouse Picking Robots
Segmentation and tracking of unseen object instances in discrete frames pose a significant challenge in dynamic industrial robotic contexts, such as distribution warehouses.
Towards Learning Geometric Eigen-Lengths Crucial for Fitting Tasks
In this work, we therefore for the first time formulate and propose a novel learning problem on this question and set up a benchmark suite including tasks, data, and evaluation metrics for studying the problem.
RHOBIN Challenge: Reconstruction of Human Object Interaction
Modeling the interaction between humans and objects has been an emerging research direction in recent years.
