BEV

This post provides a technical deep dive into the Wayformer paper [1], a key publication in the field of motion forecasting. Training Overview An overview of the deep learning training pipeline, illustrating the data flow and key components involved during model training. Model Overview of the One-Stage E2E model One staged E2E model. Overview of the Two-Stage E2E model Two staged E2E model. Details of the Two-Stage E2E Model Overview of the Wayformer model. Model Structure Overview (a) (b) The left figure shows the encoder and decoder of the Wayformer model. The right figure shows the details of the encoder [1]. Feature Embedding/Feature Projection $$\mathbf{f}\in \mathbb{R}^{T \times N\times D} \to \mathbf{x}_{input} \in \mathbb{R}^{(T \cdot N) \times d}$$Where $T$ is the number of time history, $N$ is the number of entities, $D$ is the number of features, and $d=256$. ...

This article develops end-of-line (EOL) camera extrinsic calibration and calibration-board placement for autonomous-vehicle camera rigs, including bird’s-eye-view applications. There are two linked optimization problems: estimate camera and board poses from measurements, then choose physical board positions and orientations that make the camera estimates more precise. Camera intrinsics are assumed known, and absolute extrinsics require a world-frame anchor. The presentation follows this order: General calibration problem formulation: variables, coordinate conventions, measurements, priors, and identifiability. Solver: sparse factor-graph bundle adjustment for the pose estimates. Board-position and orientation optimization formulation: camera uncertainty, design variables, constraints, and optimization methods. Experiments from simple to complex: one camera and one board, two-camera cases, a multi-camera workspace, and a vehicle rig. Geometric preprocessing, ray-plane derivations, and optional design formulations are collected in appendices. The detailed ADMM formulation and solver benchmarks are in Appendix E: ADMM and Bundle Adjustment Comparison. ...