2026 - Master Thesis - Algorithm Compression

ETH AI Center, UZH AI, PRS



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Task Definition

The essence of LiDAR-free technology can be summarized as: transforming sparse measurements of the physical world into dense geometric inference.


References






Detection and Tracking Algorithms

Layer Technical Components Application
Detection Backbone RT-DETR Transformer-based object detection producing spatially consistent bounding boxes and feature embeddings for downstream multi-view association.
Temporal Association Query-Based Tracking (e.g., MOTR-style) Maintains persistent object identities across frames using learnable spatiotemporal queries instead of heuristic matching.
Multi-View Correspondence Epipolar Geometry Filters cross-camera matches using the Fundamental Matrix to enforce geometric consistency before triangulation.
3D Reconstruction Triangulation + PnP Recovers metric 3D positions from validated multi-view 2D detections and refines camera pose estimates.
Global Optimization Bundle Adjustment Minimizes reprojection error jointly over camera poses and object trajectories to achieve globally consistent 4D reconstruction.
Dynamic Motion Modeling Motion Decomposition Separates object motion from camera motion to stabilize optimization under dynamic scenes.
Spatiotemporal Refinement Uncertainty-Aware Optimization Weighs correspondences by confidence scores to improve robustness under occlusion, noise, and adverse weather conditions.
Identity Persistence Cross-Camera Re-Identification Uses learned feature embeddings to maintain consistent object identities across disjoint camera views.







References