Search Results for author:
Found 8 papers, 6 papers with code
RFNet-4D++: Joint Object Reconstruction and Flow Estimation from 4D Point Clouds with Cross-Attention Spatio-Temporal Features
The key insight is simultaneously performing both tasks via learning of spatial and temporal features from a sequence of point clouds can leverage individual tasks, leading to improved overall performance.
Ranked #1 on
3D Human Reconstruction
on Dynamic FAUST
Point-set Distances for Learning Representations of 3D Point Clouds
From this study, we propose to use sliced Wasserstein distance and its variants for learning representations of 3D point clouds.
LCD: Learned Cross-Domain Descriptors for 2D-3D Matching
In this work, we present a novel method to learn a local cross-domain descriptor for 2D image and 3D point cloud matching.
A*3D Dataset: Towards Autonomous Driving in Challenging Environments
With the increasing global popularity of self-driving cars, there is an immediate need for challenging real-world datasets for benchmarking and training various computer vision tasks such as 3D object detection.
Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data
From our comprehensive benchmark, we show that our dataset poses great challenges to existing point cloud classification techniques as objects from real-world scans are often cluttered with background and/or are partial due to occlusions.
JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds with Multi-Task Pointwise Networks and Multi-Value Conditional Random Fields
Deep learning techniques have become the to-go models for most vision-related tasks on 2D images.
Ranked #2 on
3D Instance Segmentation
on SceneNN
3D Instance Segmentation
3D Semantic Instance Segmentation
+3
Real-time Progressive 3D Semantic Segmentation for Indoor Scene
The widespread adoption of autonomous systems such as drones and assistant robots has created a need for real-time high-quality semantic scene segmentation.
A Field Model for Repairing 3D Shapes
The proposed method was evaluated on both artificial data and real data obtained from reconstruction of practical scenes.
