TRIG: Trajectory-Rig Decoupled Metric Geometry Learning

2026-07-06

AI Summary

Existing visual geometry models in autonomous driving struggle to effectively leverage static multi-camera geometric priors from the vehicle due to their coupled modeling of time-varying ego-motion and fixed camera rig geometry. This work proposes TRIG, a novel framework that explicitly decouples camera pose into two components: the ego-vehicle trajectory and the camera rig configuration, thereby separately modeling dynamic self-motion and static multi-camera topology. TRIG introduces a decoupled pose representation, independent supervision mechanisms, and sparse spatio-temporal attention, which together preserve geometric reasoning capabilities while substantially reducing computational overhead. Evaluated across five autonomous driving benchmarks, the method achieves state-of-the-art performance in pose estimation, metric depth prediction, and 3D reconstruction.

Abstract

Vision-centric autonomous driving requires accurate metric geometry and ego-motion estimation from synchronized multi-camera observations. Recent visual geometry models show strong performance in pose estimation, depth prediction, and 3D reconstruction, but are not tailored to rigid multi-camera driving systems. They often encode camera poses as entangled representations, in which time-varying ego-motion and static camera-rig geometry are jointly modeled, limiting the utilization of vehicle-side geometric priors. We propose Trajectory-Rig Decoupled Metric Geometry Learning (TRIG), a geometry perception framework for autonomous driving. TRIG factorizes camera poses into ego-trajectory and camera-rig components, enabling separate modeling of ego-motion and static multi-camera topology. We introduce decoupled pose encoding and supervision, which separately constrain trajectory evolution and rig geometry for metric-consistent learning. Moreover, sparse Temporal--Spatial attention separates cross-camera interaction from temporal aggregation, reducing global attention cost while preserving geometric reasoning. Experiments on five autonomous driving benchmarks show that TRIG achieves state-of-the-art performance in pose estimation, metric depth prediction, and 3D reconstruction.

Problem

• metric geometry
• ego-motion
• multi-camera rig
• pose estimation
• geometry perception

Innovation

• pose decoupling
• metric geometry learning
• multi-camera rig
• ego-motion estimation
• temporal-spatial attention