Search Results for author:
Found 8 papers, 5 papers with code
3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction
Inspired by the recent success of methods that employ shape priors to achieve robust 3D reconstructions, we propose a novel recurrent neural network architecture that we call the 3D Recurrent Reconstruction Neural Network (3D-R2N2).
Ranked #4 on
3D Reconstruction
on Data3D−R2N2
Universal Correspondence Network
We present a deep learning framework for accurate visual correspondences and demonstrate its effectiveness for both geometric and semantic matching, spanning across rigid motions to intra-class shape or appearance variations.
Scene Graph Generation by Iterative Message Passing
In this work, we explicitly model the objects and their relationships using scene graphs, a visually-grounded graphical structure of an image.
Ranked #9 on
Panoptic Scene Graph Generation
on PSG Dataset
DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents
DESIRE effectively predicts future locations of objects in multiple scenes by 1) accounting for the multi-modal nature of the future prediction (i. e., given the same context, future may vary), 2) foreseeing the potential future outcomes and make a strategic prediction based on that, and 3) reasoning not only from the past motion history, but also from the scene context as well as the interactions among the agents.
Ranked #1 on
Trajectory Prediction
on PAID
Weakly supervised 3D Reconstruction with Adversarial Constraint
Supervised 3D reconstruction has witnessed a significant progress through the use of deep neural networks.
SEGCloud: Semantic Segmentation of 3D Point Clouds
Coarse voxel predictions from a 3D Fully Convolutional NN are transferred back to the raw 3D points via trilinear interpolation.
Ranked #13 on
Semantic Segmentation
on Semantic3D
Text2Shape: Generating Shapes from Natural Language by Learning Joint Embeddings
To this end, we first learn joint embeddings of freeform text descriptions and colored 3D shapes.
Adversarially Robust 3D Point Cloud Recognition Using Self-Supervisions
In this paper, we systematically study the impact of various self-supervised learning proxy tasks on different architectures and threat models for 3D point clouds with adversarial training.
