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Found 10 papers, 0 papers with code
FM-Fusion: Instance-aware Semantic Mapping Boosted by Vision-Language Foundation Models
Our method achieves 40. 3 mean average precision (mAP) on the ScanNet semantic instance segmentation task.
Are All Point Clouds Suitable for Completion? Weakly Supervised Quality Evaluation Network for Point Cloud Completion
In this paper, we propose a quality evaluation network to score the point clouds and help judge the quality of the point cloud before applying the completion model.
Efficient Implicit Neural Reconstruction Using LiDAR
Modeling scene geometry using implicit neural representation has revealed its advantages in accuracy, flexibility, and low memory usage.
You Only Label Once: 3D Box Adaptation from Point Cloud to Image via Semi-Supervised Learning
The image-based 3D object detection task expects that the predicted 3D bounding box has a ``tightness'' projection (also referred to as cuboid), which fits the object contour well on the image while still keeping the geometric attribute on the 3D space, e. g., physical dimension, pairwise orthogonal, etc.
Temporal Point Cloud Completion with Pose Disturbance
With the help of gated recovery units(GRU) and attention mechanisms as temporal units, we propose a point cloud completion framework that accepts a sequence of unaligned and sparse inputs, and outputs consistent and aligned point clouds.
Graph-Guided Deformation for Point Cloud Completion
In this paper, we propose a Graph-Guided Deformation Network, which respectively regards the input data and intermediate generation as controlling and supporting points, and models the optimization guided by a graph convolutional network(GCN) for the point cloud completion task.
Tracking from Patterns: Learning Corresponding Patterns in Point Clouds for 3D Object Tracking
A robust 3D object tracker which continuously tracks surrounding objects and estimates their trajectories is key for self-driving vehicles.
Joint Spatial-Temporal Optimization for Stereo 3D Object Tracking
Directly learning multiple 3D objects motion from sequential images is difficult, while the geometric bundle adjustment lacks the ability to localize the invisible object centroid.
Coarse-To-Fine Visual Localization Using Semantic Compact Map
Robust visual localization for urban vehicles remains challenging and unsolved.
DF-SLAM: A Deep-Learning Enhanced Visual SLAM System based on Deep Local Features
As the foundation of driverless vehicle and intelligent robots, Simultaneous Localization and Mapping(SLAM) has attracted much attention these days.
