Search Results for author:
Found 11 papers, 6 papers with code
Learning the Geodesic Embedding with Graph Neural Networks
Then, we can compute the geodesic distance between a pair of points using our decoding function, which requires only several matrix multiplications and can be massively parallelized on GPUs.
6D-ViT: Category-Level 6D Object Pose Estimation via Transformer-based Instance Representation Learning
Specifically, a novel two-stream encoder-decoder framework is dedicated to exploring complex and powerful instance representations from RGB images, point clouds and categorical shape priors.
AA-RMVSNet: Adaptive Aggregation Recurrent Multi-view Stereo Network
To overcome the difficulty of varying occlusion in complex scenes, we propose an inter-view cost volume aggregation module for adaptive pixel-wise view aggregation, which is able to preserve better-matched pairs among all views.
Ranked #10 on
Point Clouds
on Tanks and Temples
(Mean F1 (Intermediate) metric)
Deep Learning for Multi-View Stereo via Plane Sweep: A Survey
3D reconstruction has lately attracted increasing attention due to its wide application in many areas, such as autonomous driving, robotics and virtual reality.
Dense Hybrid Recurrent Multi-view Stereo Net with Dynamic Consistency Checking
In this paper, we propose an efficient and effective dense hybrid recurrent multi-view stereo net with dynamic consistency checking, namely $D^{2}$HC-RMVSNet, for accurate dense point cloud reconstruction.
SegVoxelNet: Exploring Semantic Context and Depth-aware Features for 3D Vehicle Detection from Point Cloud
First, the semantic context information in LiDAR is seldom explored in previous works, which may help identify ambiguous vehicles.
Graph-Based Parallel Large Scale Structure from Motion
After local reconstructions, we construct a minimum spanning tree (MinST) to find accurate similarity transformations.
Bundle Adjustment Revisited
3D reconstruction has been developing all these two decades, from moderate to medium size and to large scale.
Pyramid Multi-view Stereo Net with Self-adaptive View Aggregation
n this paper, we propose an effective and efficient pyramid multi-view stereo (MVS) net with self-adaptive view aggregation for accurate and complete dense point cloud reconstruction.
AADS: Augmented Autonomous Driving Simulation using Data-driven Algorithms
Our augmented approach combines the flexibility in a virtual environment (e. g., vehicle movements) with the richness of the real world to allow effective simulation of anywhere on earth.
X-GANs: Image Reconstruction Made Easy for Extreme Cases
Image reconstruction including image restoration and denoising is a challenging problem in the field of image computing.
