  We propose a new object-centric framework for learning-based stereo 3D object
detection. Previous studies build scene-centric representations that do not
consider the significant variation among outdoor instances and thus lack the
flexibility and functionalities that an instance-level model can offer. We
build such an instance-level model by formulating and tackling a local update
problem, i.e., how to predict a refined update given an initial 3D cuboid
guess. We demonstrate how solving this problem can complement scene-centric
approaches in (i) building a coarse-to-fine multi-resolution system, (ii)
performing model-agnostic object location refinement, and (iii) conducting
stereo 3D tracking-by-detection. Extensive experiments demonstrate the
effectiveness of our approach, which achieves state-of-the-art performance on
the KITTI benchmark. Code and pre-trained models are available at
https://github.com/Nicholasli1995/SNVC.

Computer Vision and Pattern Recognition;Robotics