Understanding Depth Map Progressively: Adaptive Distance Interval
  Separation for Monocular 3d Object Detection
Xianhui Cheng Shoumeng Qiu Zhikang Zou Jian Pu Xiangyang Xue
Monocular 3D object detection aims to locate objects in different scenes with
just a single image. Due to the absence of depth information, several monocular
3D detection techniques have emerged that rely on auxiliary depth maps from the
depth estimation task. There are multiple approaches to understanding the
representation of depth maps, including treating them as pseudo-LiDAR point
clouds, leveraging implicit end-to-end learning of depth information, or
considering them as an image input. However, these methods have certain
drawbacks, such as their reliance on the accuracy of estimated depth maps and
suboptimal utilization of depth maps due to their image-based nature. While
LiDAR-based methods and convolutional neural networks (CNNs) can be utilized
for pseudo point clouds and depth maps, respectively, it is always an
alternative. In this paper, we propose a framework named the Adaptive Distance
Interval Separation Network (ADISN) that adopts a novel perspective on
understanding depth maps, as a form that lies between LiDAR and images. We
utilize an adaptive separation approach that partitions the depth map into
various subgraphs based on distance and treats each of these subgraphs as an
individual image for feature extraction. After adaptive separations, each
subgraph solely contains pixels within a learned interval range. If there is a
truncated object within this range, an evident curved edge will appear, which
we can leverage for texture extraction using CNNs to obtain rich depth
information in pixels. Meanwhile, to mitigate the inaccuracy of depth
estimation, we designed an uncertainty module. To take advantage of both images
and depth maps, we use different branches to learn localization detection tasks
and appearance tasks separately.