Search Results for author:
Found 17 papers, 9 papers with code
Sub-Meter Remote Sensing of Soil Moisture Using Portable L-band Microwave Radiometer
Spaceborne microwave passive soil moisture products are known for their accuracy but are often limited by coarse spatial resolutions.
DreamMat: High-quality PBR Material Generation with Geometry- and Light-aware Diffusion Models
2D diffusion model, which often contains unwanted baked-in shading effects and results in unrealistic rendering effects in the downstream applications.
Image Content Generation with Causal Reasoning
Considering causal reasoning in visual content generation is significant.
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.
SoccerNet 2022 Challenges Results
The SoccerNet 2022 challenges were the second annual video understanding challenges organized by the SoccerNet team.
Lung-Originated Tumor Segmentation from Computed Tomography Scan (LOTUS) Benchmark
Lung cancer is one of the deadliest cancers, and in part its effective diagnosis and treatment depend on the accurate delineation of the tumor.
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).
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.
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.
Beyond Photometric Loss for Self-Supervised Ego-Motion Estimation
Accurate relative pose is one of the key components in visual odometry (VO) and simultaneous localization and mapping (SLAM).
Ranked #3 on
Camera Pose Estimation
on KITTI Odometry Benchmark
Matchable Image Retrieval by Learning from Surface Reconstruction
Convolutional Neural Networks (CNNs) have achieved superior performance on object image retrieval, while Bag-of-Words (BoW) models with handcrafted local features still dominate the retrieval of overlapping images in 3D reconstruction.
GeoDesc: Learning Local Descriptors by Integrating Geometry Constraints
Learned local descriptors based on Convolutional Neural Networks (CNNs) have achieved significant improvements on patch-based benchmarks, whereas not having demonstrated strong generalization ability on recent benchmarks of image-based 3D reconstruction.
Learning and Matching Multi-View Descriptors for Registration of Point Clouds
Critical to the registration of point clouds is the establishment of a set of accurate correspondences between points in 3D space.
Very Large-Scale Global SfM by Distributed Motion Averaging
This work proposes a divide-and-conquer framework to solve very large global SfM at the scale of millions of images.
Distributed Very Large Scale Bundle Adjustment by Global Camera Consensus
In this paper, we propose a distributed approach to coping with this global bundle adjustment for very large scale Structure-from-Motion computation.
Parallel Structure from Motion from Local Increment to Global Averaging
In this paper, we tackle the accurate and consistent Structure from Motion (SfM) problem, in particular camera registration, far exceeding the memory of a single computer in parallel.
Joint Camera Clustering and Surface Segmentation for Large-Scale Multi-View Stereo
To solve this problem, we propose a joint optimization in a hierarchical framework to obtain the final surface segments and corresponding optimal camera clusters.
