ステレオ画像からの3D物体検出と暗黙的表面再構成の統合フレームワーク
Joint stereo 3D object detection and implicit surface reconstruction
ステレオRGB画像から物体の3D姿勢を推定しつつ、点ベース表現で暗黙的な剛体形状を再構成する学習フレームワークS-3D-RCNNを提案した論文。
著者: Shichao Li, Xijie Huang, Zechun Liu, Kwang-Ting Cheng
分類: cs.CV, cs.GR, cs.RO
原文アブストラクト
We present a new learning-based framework S-3D-RCNN that can recover accurate object orientation in SO(3) and simultaneously predict implicit rigid shapes from stereo RGB images. For orientation estimation, in contrast to previous studies that map local appearance to observation angles, we propose a progressive approach by extracting meaningful Intermediate Geometrical Representations (IGRs). This approach features a deep model that transforms perceived intensities from one or two views to object part coordinates to achieve direct egocentric object orientation estimation in the camera coordinate system. To further achieve finer description inside 3D bounding boxes, we investigate the implicit shape estimation problem from stereo images. We model visible object surfaces by designing a point-based representation, augmenting IGRs to explicitly address the unseen surface hallucination problem. Extensive experiments validate the effectiveness of the proposed IGRs, and S-3D-RCNN achieves superior 3D scene understanding performance. We also designed new metrics on the KITTI benchmark for our evaluation of implicit shape estimation.
