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Found 6 papers, 0 papers with code
Towards Unsupervised Object Detection From LiDAR Point Clouds
In this paper, we study the problem of unsupervised object detection from 3D point clouds in self-driving scenes.
CADSim: Robust and Scalable in-the-wild 3D Reconstruction for Controllable Sensor Simulation
To tackle these issues, we present CADSim, which combines part-aware object-class priors via a small set of CAD models with differentiable rendering to automatically reconstruct vehicle geometry, including articulated wheels, with high-quality appearance.
LabelFormer: Object Trajectory Refinement for Offboard Perception from LiDAR Point Clouds
Auto-labels are most commonly generated via a two-stage approach -- first objects are detected and tracked over time, and then each object trajectory is passed to a learned refinement model to improve accuracy.
UniSim: A Neural Closed-Loop Sensor Simulator
Previously recorded driving logs provide a rich resource to build these new scenarios from, but for closed loop evaluation, we need to modify the sensor data based on the new scene configuration and the SDV's decisions, as actors might be added or removed and the trajectories of existing actors and the SDV will differ from the original log.
Virtual Correspondence: Humans as a Cue for Extreme-View Geometry
Similar to classic correspondences, VCs conform with epipolar geometry; unlike classic correspondences, VCs do not need to be co-visible across views.
Asynchronous Multi-View SLAM
Existing multi-camera SLAM systems assume synchronized shutters for all cameras, which is often not the case in practice.
