ECCV 2018気になる論文

ECCV 2018で発表される気になる論文です。主に3次元再構成系です。

eccv2018.org

Oral

• MVSNet: Depth Inference for Unstructured Multi-view Stereo
• PlaneMatch: Patch Coplanarity Prediction for Robust RGB-D Registrati
• Active Stereo Net: End-to-End Self-Supervised Learning for Active Stereo Systems
• GAL: Geometric Adversarial Loss for Single-View 3D-Object Reconstruction
• DeepTAM: Deep Tracking and Mapping

Poster

• Semi-Dense 3D Reconstruction with a Stereo Event Camera
• Geometric Constrained Joint Lane Segmentation and Lane Boundary Detection
• Good Line Cutting: towards Accurate Pose Tracking of Line-assisted VO/VSLAM
• Fully-Convolutional Point Networks for Large-Scale Point Clouds
• Monocular Depth Estimation Using Whole Strip Masking and Reliability-Based Refinement
• Linear RGB-D SLAM for Planar Environments
• Estimating Depth from RGB and Sparse Sensing
• Into the Twilight Zone: Depth Estimation using Joint Structure-Stereo Optimization
• Layer-structured 3D Scene Inference via View Synthesis
• ArticulatedFusion: Real-time Reconstruction of Motion, Geometry and Segmentation Using a Single Depth Camera
• Monocular Scene Parsing and Reconstruction using 3D Holistic Scene Grammar
• Recovering 3D Planes from a Single Image via Convolutional Neural Networks
• 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation
• Learning Priors for Semantic 3D Reconstruction
• Large Scale Urban Scene Modeling from MVS Meshes
• Learning Category-Specific Mesh Reconstruction from Image Collections
• StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth Prediction
• Semantically Aware Urban 3D Reconstruction with Plane-Based Regularization
• BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation
• RIDI: Robust IMU Double Integration
• Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network
• CornerNet: Detecting Objects as Paired Keypoints
• A Unified Framework for Single-View 3D Reconstruction with Limited Pose Supervision
• A Minimal Closed-Form Solution for Multi-Perspective Pose Estimation using Points and Lines
• Learn-to-Score: Efficient 3D Scene Exploration by Predicting View Utility
• 3D Ego-Pose Estimation via Imitation Learning
• Look Deeper into Depth: Monocular Depth Estimation with Semantic Booster and Attention-Driven Loss