Search Results for author:
Found 11 papers, 6 papers with code
Benchmarks and Challenges in Pose Estimation for Egocentric Hand Interactions with Objects
We interact with the world with our hands and see it through our own (egocentric) perspective.
Mitigating Perspective Distortion-induced Shape Ambiguity in Image Crops
Objects undergo varying amounts of perspective distortion as they move across a camera's field of view.
3D Hand Pose Estimation in Egocentric Images in the Wild
We present WildHands, a method for 3D hand pose estimation in egocentric images in the wild.
Learning Hand-Held Object Reconstruction from In-The-Wild Videos
Prior works for reconstructing hand-held objects from a single image rely on direct 3D shape supervision which is challenging to gather in real world at scale.
TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving
At the time of submission, TransFuser outperforms all prior work on the CARLA leaderboard in terms of driving score by a large margin.
Ranked #6 on
Autonomous Driving
on CARLA Leaderboard
NEAT: Neural Attention Fields for End-to-End Autonomous Driving
Efficient reasoning about the semantic, spatial, and temporal structure of a scene is a crucial prerequisite for autonomous driving.
Ranked #4 on
Novel View Synthesis
on X3D
Multi-Modal Fusion Transformer for End-to-End Autonomous Driving
How should representations from complementary sensors be integrated for autonomous driving?
Ranked #1 on
Autonomous Driving
on Town05 Short
Exploring Data Aggregation in Policy Learning for Vision-Based Urban Autonomous Driving
Data aggregation techniques can significantly improve vision-based policy learning within a training environment, e. g., learning to drive in a specific simulation condition.
Label Efficient Visual Abstractions for Autonomous Driving
Beyond label efficiency, we find several additional training benefits when leveraging visual abstractions, such as a significant reduction in the variance of the learned policy when compared to state-of-the-art end-to-end driving models.
Deep Fundamental Matrix Estimation without Correspondences
Estimating fundamental matrices is a classic problem in computer vision.
iSPA-Net: Iterative Semantic Pose Alignment Network
Such image comparison based approach also alleviates the problem of data scarcity and hence enhances scalability of the proposed approach for novel object categories with minimal annotation.
