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 $d=256$. ...

In this post, we will discuss the end-of-line (EOL) camera calibration, especially for camera bird’s-eye view (BEV) extrinsic calibration. Suggested Pipeline Do single camera intrinsic calibration Measure each corner in the world coordinate Refine the corners’ coordinates according to board constraints (plane, parallel, equally spaced) Find the plane equation in the world coordinate Initialize each camera’s extrinsic parameters by solving the perspective-n-point (PnP) problem Estimate the 3D coordinates by the intersection of the image ray and board plane ...