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- W4200532064 abstract "Vision-based 3D object detection is a research focus in the field of autonomous driving system. While recently proposed pseudo-LiDAR is a promising solution, its performance is severely restricted by the image-based depth estimator, leading to a considerable performance gap against the LiDAR-based counterparts. In this paper, substantial advances are developed along an orthogonal direction to the previous efforts in the pseudo-LiDAR pipeline. Concretely, we propose a plug- and-play module, called Online Semantic Embedding (OSE), aligning image semantics with the pseudo-LiDAR detection in an end-to-end manner. On the KITTI object detection benchmark, existing stereo-based baselines integrated with our approach show impressive improvements without bells and whistles. Furthermore, we emphasize that OSE works in retrieving the performance under geometric imperfection conditions." @default.
- W4200532064 created "2021-12-31" @default.
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- W4200532064 date "2021-09-27" @default.
- W4200532064 modified "2023-10-16" @default.
- W4200532064 title "Look Before You Act: Boosting Pseudo-LiDAR with Online Semantic Embedding" @default.
- W4200532064 doi "https://doi.org/10.1109/iros51168.2021.9636529" @default.
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