Map-Det3D: ストリーミング入力からの多視点3D物体検出のためのメトリックフィードフォワード3D再構築事前知識
Map-Det3D: Metric Feed-Forward 3D Reconstruction Prior for Multi-view 3D Object Detection from Streaming Inputs
単眼ビデオから直接メトリック3D空間で物体検出を行うため、RGBから3D再構築するフィードフォワードモデルを幾何学的バックボーンとして利用し、2Dから3Dへのリフティングを回避する新しいオンライン多視点3D物体検出モデルを提案した。
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著者: Yung-Hsu Yang, Luigi Piccinelli, Samuel Rota Bulò, Sunghwan Hong, Denis Rozumny, Johannes Schönberger, Zuria Bauer, Hermann Blum, Peter Kontschieder, Marc Pollefeys
分類: cs.CV
原文アブストラクト
Metric 3D object detection is a core capability for embodied agents, yet most reliable systems lean on depth sensors, trading away cost, power, and integration simplicity. This motivates monocular 3D detection, which avoids additional constraints, yet it faces a major obstacle: from a single image, depth, and especially absolute scale, are underconstrained. As a result, the prevailing pattern of detecting in 2D and then predicting 3D attributes is often brittle, since modest range errors can dominate 3D localization, and the learned scale prior can fail when cameras, motion, or environments undergo domain shifts. To address this, we propose Map-Det3D, an online multi-view 3D object detection model that brings detection directly into a 3D space reconstructed from RGB. We map a short temporal window into multiple views and repurpose a feed-forward metric 3D reconstruction model as our geometric backbone while tuning its object-aware capabilities. Building on this representation, Map-Det3D directly predicts boxes in metric 3D space, without the widely used 2D-to-3D lifting. Experiments across different benchmarks show that this design supports strong online performance and robust transfer without adaptation, suggesting that training reconstruction priors for detection is a practical route to stable metric 3D detection from monocular video. Code and models are available at https://royyang0714.github.io/Map-Det3D.