Q&A

1

What is standard dense supervised learning? Mentioned in [[CenterNet]].

Standard dense supervised learning typically refers to a supervised learning setup where:

1. Standard supervised learning means:
   • You have input data X and corresponding ground truth labels Y.
   • The goal is to train a model $f_\theta(X)$ that maps inputs to outputs by minimizing a loss function (e.g., cross-entropy, MSE) between the predicted labels and ground truth.
   • The training dataset is fully labeled (i.e., each input has a corresponding label).
2. Dense refers to:
   • A per-pixel or per-element prediction task, where every element in the input gets a corresponding label.
   • Common in vision tasks like:
     • Semantic segmentation (each pixel is labeled with a class).
     • Depth estimation (each pixel has a depth value).
     • Optical flow (each pixel has a motion vector).
     • Surface normal estimation (each pixel has a 3D orientation vector).

In contrast to sparse supervision, where only a subset of the input (e.g., bounding boxes, keypoints) is labeled, dense supervision provides full annotations for every relevant part of the input.

Example
In semantic segmentation:

• Input: an RGB image (e.g., 512×512 pixels).
• Output: a label map of the same size (512×512), where each pixel has a class label like “road”, “car”, “sky”, etc.
• Model: often a Fully Convolutional Network (FCN) or encoder-decoder like U-Net or DeepLab.
• Loss: usually pixel-wise cross-entropy.

Goal

Estimate accurate 6D poses of multiple known objects in a 3D scene captured by multiple cameras with unknown positions

Challenges

• object pose hypotheses made in individual images cannot easily be expressed in a common reference frame when the relative transformations between the cameras are unknown(相机相对位置未知)
• the single-view 6D object pose hypotheses have gross errors in the form of false positive and missed detections（由于视角遮蔽，会存在误报和错漏的情况）
• the candidate 6D object poses estimated from input images are noisy as they suffer from depth ambiguities inherent to single view methods.（深度信息通常没那么精准）

Approach