单目深度估计测验

当您参加测验时，就会出现答案和解释。

1. 是什么让深度估计成为“单目”？
   • 它从单个图像或相机推断深度，而不是立体或传感器
   • 它使用两个像人眼一样间隔的摄像头
   • 它仅适用于黑白图像
   • 它需要连接到相机的激光雷达扫描仪
2. 单目深度估计器对图像预测什么？
   • A stereo disparity field that requires two camera views
   • A per-pixel depth or relative-depth map from one camera view
   • A categorical object-label map with no distance estimate
   • A dense motion-vector field between two video frames
3. Depth Anything V2 论文报告了什么数据配方？
   • Train only on real labeled images and discard synthetic data
   • Train on unlabeled audio and convert speech to depth maps
   • Train a DINOv2-G teacher on synthetic labeled images, then use its pseudo-labels on real images to train students
   • Use stereo disparity as labels for every image at inference time
4. 单目网络不直接依赖哪种线索来获得深度？
   • 物体的相对大小
   • 纹理渐变和透视
   • 遮挡（哪个物体阻挡另一个物体）
   • 两个同步摄像机之间的立体视差
5. Monodepth2 使用什么来减少移动物体在自监督训练中的影响？
   • An auto-masking procedure that excludes pixels inconsistent with camera motion
   • A learned text tokenizer
   • A region-proposal network
   • A fixed depth value for every object
6. What new evaluation benchmark does the Depth Anything V2 paper introduce?
   • A benchmark of 2,000 full metric depth maps from LiDAR only
   • DA-2K, with sparse relative-depth pairs annotated across 1,000 images
   • A corpus of six public datasets and randomly captured photos
   • A camera-calibration set containing no depth comparisons
7. How can self-supervised monocular depth training obtain a learning signal?
   • By reading depth labels from every pixel
   • By converting image colors directly to meters
   • By reconstructing one view from adjacent video frames and penalizing photometric mismatch
   • By using only language captions
8. What does a relative-depth output indicate?
   • Exact meters for every pixel
   • Camera GPS position
   • Object identity only
   • Ordering or relative distance, not necessarily calibrated metric distance