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Technical Report for Argoverse Challenges on Unified Sensor-based Detection, Tracking, and Forecasting

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arxiv 2311.15615 v1 pith:ZGRSXDBC submitted 2023-11-27 cs.CV

classification cs.CV
keywords forecastingdetectiontrackingargoverseunifiedchallengessolutioncvpr
verification ladder T0 review T1 audit T2 compute T3 formal
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This report presents our Le3DE2E solution for unified sensor-based detection, tracking, and forecasting in Argoverse Challenges at CVPR 2023 Workshop on Autonomous Driving (WAD). We propose a unified network that incorporates three tasks, including detection, tracking, and forecasting. This solution adopts a strong Bird's Eye View (BEV) encoder with spatial and temporal fusion and generates unified representations for multi-tasks. The solution was tested in the Argoverse 2 sensor dataset to evaluate the detection, tracking, and forecasting of 26 object categories. We achieved 1st place in Detection, Tracking, and Forecasting on the E2E Forecasting track in Argoverse Challenges at CVPR 2023 WAD.

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Cited by 2 Pith papers

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  1. SynFlow: Scaling Up LiDAR Scene Flow Estimation with Synthetic Data

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    Training scene flow networks on 940k synthetic CARLA LiDAR frames transfers zero-shot to real benchmarks and makes 5% real labels beat 20%.

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    cs.CV 2026-01 conditional novelty 5.0 of 10

    A new gated deformable fusion mechanism for query-based camera–LiDAR perception-and-prediction reports improved false-positive ratio (0.147) and EPA (0.335) on nuScenes over Li-ViP3D.

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