  Camera-based 3D object detectors are welcome due to their wider deployment
and lower price than LiDAR sensors. We first revisit the prior stereo detector
DSGN for its stereo volume construction ways for representing both 3D geometry
and semantics. We polish the stereo modeling and propose the advanced version,
DSGN++, aiming to enhance effective information flow throughout the 2D-to-3D
pipeline in three main aspects. First, to effectively lift the 2D information
to stereo volume, we propose depth-wise plane sweeping (DPS) that allows denser
connections and extracts depth-guided features. Second, for grasping
differently spaced features, we present a novel stereo volume -- Dual-view
Stereo Volume (DSV) that integrates front-view and top-view features and
reconstructs sub-voxel depth in the camera frustum. Third, as the foreground
region becomes less dominant in 3D space, we propose a multi-modal data editing
strategy -- Stereo-LiDAR Copy-Paste, which ensures cross-modal alignment and
improves data efficiency. Without bells and whistles, extensive experiments in
various modality setups on the popular KITTI benchmark show that our method
consistently outperforms other camera-based 3D detectors for all categories.
Code is available at https://github.com/chenyilun95/DSGN2.
