Improving Distant 3D Object Detection Using 2D Box Supervision
Zetong Yang Zhiding Yu Chris Choy Renhao Wang Anima Anandkumar Jose M. Alvarez
Improving the detection of distant 3d objects is an important yet challenging
task. For camera-based 3D perception, the annotation of 3d bounding relies
heavily on LiDAR for accurate depth information. As such, the distance of
annotation is often limited due to the sparsity of LiDAR points on distant
objects, which hampers the capability of existing detectors for long-range
scenarios. We address this challenge by considering only 2D box supervision for
distant objects since they are easy to annotate. We propose LR3D, a framework
that learns to recover the missing depth of distant objects. LR3D adopts an
implicit projection head to learn the generation of mapping between 2D boxes
and depth using the 3D supervision on close objects. This mapping allows the
depth estimation of distant objects conditioned on their 2D boxes, making
long-range 3D detection with 2D supervision feasible. Experiments show that
without distant 3D annotations, LR3D allows camera-based methods to detect
distant objects (over 200m) with comparable accuracy to full 3D supervision.
Our framework is general, and could widely benefit 3D detection methods to a
large extent.