DDStereo: Efficient Dual Decoder Transformers for Stereo 3D Road Anomaly Detection

• URL: http://arxiv.org/abs/2606.24805v1
• Date: Tue, 23 Jun 2026 16:54:11 GMT
• Title: DDStereo: Efficient Dual Decoder Transformers for Stereo 3D Road Anomaly Detection
• Abstract summary: DDStereo is a novel Dual-Decoder Stereo Transformer for real-time open-set 3D object detection.<n>Our method surpasses existing stereo 3D detectors in inference speed and, for the first time, achieves real-time performance comparable to monocular approaches.
• Score: 19.129272402040915
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Stereo-based 3D object detection still faces two critical safety challenges: real-time performance and open-set generalization. Existing stereo 3D methods typically achieve twice the accuracy of monocular methods but suffer from significantly lower inference speeds, making them unsuitable for real-time applications. Meanwhile, recent advances in open-world detection have introduced open-set and open-vocabulary algorithms in monocular 2D and 3D settings, yet stereo-based open-set detection remains largely unexplored. To bridge this gap, we propose DDStereo, a novel Dual-Decoder Stereo Transformer for real-time open-set 3D object detection. DDStereo features two lightweight decoder branches: one for open-set foreground 2D detection and the other for 3D attribute regression. These decoders share object-level queries to achieve unified target-level alignment. To enhance inference efficiency, we designed a compact disparity feature extractor and a streamlined decoder architecture. Experiments on public stereo 3D benchmarks demonstrate that DDStereo achieves state-of-the-art accuracy under both closed-set and open-set protocols. Notably, our method surpasses existing stereo 3D detectors in inference speed and, for the first time, achieves real-time performance comparable to monocular approaches.

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