Search Results for author:
Found 16 papers, 4 papers with code
Wolf: Captioning Everything with a World Summarization Framework
Finally, we establish a benchmark for video captioning and introduce a leaderboard, aiming to accelerate advancements in video understanding, captioning, and data alignment.
Trajeglish: Traffic Modeling as Next-Token Prediction
A longstanding challenge for self-driving development is simulating dynamic driving scenarios seeded from recorded driving logs.
Towards Viewpoint Robustness in Bird's Eye View Segmentation
We introduce a technique for novel view synthesis and use it to transform collected data to the viewpoint of target rigs, allowing us to train BEV segmentation models for diverse target rigs without any additional data collection or labeling cost.
End-to-end 3D Tracking with Decoupled Queries
In this work, we present an end-to-end framework for camera-based 3D multi-object tracking, called DQTrack.
How Much More Data Do I Need? Estimating Requirements for Downstream Tasks
Given a small training data set and a learning algorithm, how much more data is necessary to reach a target validation or test performance?
M$^2$BEV: Multi-Camera Joint 3D Detection and Segmentation with Unified Birds-Eye View Representation
In this paper, we propose M$^2$BEV, a unified framework that jointly performs 3D object detection and map segmentation in the Birds Eye View~(BEV) space with multi-camera image inputs.
Generating Useful Accident-Prone Driving Scenarios via a Learned Traffic Prior
Scenario generation is formulated as an optimization in the latent space of this traffic model, perturbing an initial real-world scene to produce trajectories that collide with a given planner.
Towards Optimal Strategies for Training Self-Driving Perception Models in Simulation
Alternative solutions seek to exploit driving simulators that can generate large amounts of labeled data with a plethora of content variations.
Learning Indoor Inverse Rendering with 3D Spatially-Varying Lighting
In this paper, we propose a unified, learning-based inverse rendering framework that formulates 3D spatially-varying lighting.
DriveGAN: Towards a Controllable High-Quality Neural Simulation
Realistic simulators are critical for training and verifying robotics systems.
Emergent Road Rules In Multi-Agent Driving Environments
For autonomous vehicles to safely share the road with human drivers, autonomous vehicles must abide by specific "road rules" that human drivers have agreed to follow.
The efficacy of Neural Planning Metrics: A meta-analysis of PKL on nuScenes
A high-performing object detection system plays a crucial role in autonomous driving (AD).
Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3D
By training on the entire camera rig, we provide evidence that our model is able to learn not only how to represent images but how to fuse predictions from all cameras into a single cohesive representation of the scene while being robust to calibration error.
Ranked #6 on
Bird's-Eye View Semantic Segmentation
on nuScenes
(IoU ped - 224x480 - Vis filter. - 100x100 at 0.5 metric)
Autonomous Vehicles
Bird's-Eye View Semantic Segmentation
+1
Learning to Simulate Dynamic Environments with GameGAN
Simulation is a crucial component of any robotic system.
Learning to Evaluate Perception Models Using Planner-Centric Metrics
The downside of these metrics is that, at worst, they penalize all incorrect detections equally without conditioning on the task or scene, and at best, heuristics need to be chosen to ensure that different mistakes count differently.
FastDraw: Addressing the Long Tail of Lane Detection by Adapting a Sequential Prediction Network
In this paper, we use lane detection to study modeling and training techniques that yield better performance on real world test drives.
