Search Results for author:
Found 12 papers, 8 papers with code
Higher-Order CRF Structural Segmentation of 3D Reconstructed Surfaces
In this paper, we propose a structural segmentation algorithm to partition multi-view stereo reconstructed surfaces of large-scale urban environments into structural segments.
Sketch-R2CNN: An Attentive Network for Vector Sketch Recognition
To bridge the gap between these two spaces in neural networks, we propose a neural line rasterization module to convert the vector sketch along with the attention estimated by RNN into a bitmap image, which is subsequently consumed by CNN.
SketchDesc: Learning Local Sketch Descriptors for Multi-view Correspondence
In this paper, we study the problem of multi-view sketch correspondence, where we take as input multiple freehand sketches with different views of the same object and predict as output the semantic correspondence among the sketches.
D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features
In this paper, we leverage a 3D fully convolutional network for 3D point clouds, and propose a novel and practical learning mechanism that densely predicts both a detection score and a description feature for each 3D point.
Ranked #2 on
Point Cloud Registration
on KITTI
End-to-End Learning Local Multi-view Descriptors for 3D Point Clouds
In this work, we propose an end-to-end framework to learn local multi-view descriptors for 3D point clouds.
Ranked #5 on
Point Cloud Registration
on 3DMatch Benchmark
JSENet: Joint Semantic Segmentation and Edge Detection Network for 3D Point Clouds
Semantic segmentation and semantic edge detection can be seen as two dual problems with close relationships in computer vision.
Ranked #31 on
Semantic Segmentation
on S3DIS
Normalized Human Pose Features for Human Action Video Alignment
We present a novel approach for extracting human pose features from human action videos.
PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency
Removing outlier correspondences is one of the critical steps for successful feature-based point cloud registration.
VMNet: Voxel-Mesh Network for Geodesic-Aware 3D Semantic Segmentation
Experimental results validate the effectiveness of VMNet: specifically, on the challenging ScanNet dataset for large-scale segmentation of indoor scenes, it outperforms the state-of-the-art SparseConvNet and MinkowskiNet (74. 6% vs 72. 5% and 73. 6% in mIoU) with a simpler network structure (17M vs 30M and 38M parameters).
Learning to Match Features with Seeded Graph Matching Network
2) Seeded Graph Neural Network, which utilizes seed matches to pass messages within/across images and predicts assignment costs.
TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers
The attention mechanism of the transformer enables our model to adaptively determine where and what information should be taken from the image, leading to a robust and effective fusion strategy.
Ranked #3 on
3D Object Detection
on nuScenes LiDAR only
LiDAL: Inter-frame Uncertainty Based Active Learning for 3D LiDAR Semantic Segmentation
We propose LiDAL, a novel active learning method for 3D LiDAR semantic segmentation by exploiting inter-frame uncertainty among LiDAR frames.
