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Found 7 papers, 3 papers with code
LASA: Instance Reconstruction from Real Scans using A Large-scale Aligned Shape Annotation Dataset
Instance shape reconstruction from a 3D scene involves recovering the full geometries of multiple objects at the semantic instance level.
Sample Complexity of Opinion Formation on Networks
In this paper, we initialize the study of sample complexity in opinion formation to solve this problem.
Towards Optimal Regret in Adversarial Linear MDPs with Bandit Feedback
The first algorithm, although computationally inefficient, ensures a regret of $\widetilde{\mathcal{O}}\left(\sqrt{K}\right)$, where $K$ is the number of episodes.
MVImgNet: A Large-scale Dataset of Multi-view Images
The birth of ImageNet drives a remarkable trend of "learning from large-scale data" in computer vision.
Towards High-Fidelity Single-view Holistic Reconstruction of Indoor Scenes
We present a new framework to reconstruct holistic 3D indoor scenes including both room background and indoor objects from single-view images.
TO-Scene: A Large-scale Dataset for Understanding 3D Tabletop Scenes
Experiments show that the algorithms trained on TO-Scene indeed work on the realistic test data, and our proposed tabletop-aware learning strategy greatly improves the state-of-the-art results on both 3D semantic segmentation and object detection tasks.
Refer-it-in-RGBD: A Bottom-up Approach for 3D Visual Grounding in RGBD Images
Grounding referring expressions in RGBD image has been an emerging field.
