FLM-Occ: フィードフォワード尤度最大化による効率的な屋内占有予測
FLM-Occ: Feed-forward Likelihood Maximization for Efficient Indoor Occupancy Prediction
屋内占有予測をボクセル分布推定として再定式化し、ガウスプリミティブの分布を大域的に最適化する新しいフレームワークを提案。従来の局所的なボクセル分類に基づく手法より少ないプリミティブで高精度かつ高速な予測を実現した。
詳しい要約
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著者: Guangcheng Chen, Lihuang Fang, Huaqi Tao, Yicheng He, Li He, Hong Zhang
分類: cs.CV, cs.RO
原文アブストラクト
Recent indoor occupancy prediction methods adopt Gaussian primitives as a sparse 3D representation for computational efficiency. However, their training relies on voxel classification, which imposes only local constraints and lacks global supervision on the distribution of the primitives. Therefore, they inevitably predict spurious primitives in empty regions, undermining both representational and computational efficiency. To address this, we propose Feed-forward Likelihood Maximization (FLM), a novel framework that reformulates occupancy prediction as voxel distribution estimation. In FLM, a network is trained to predict a mixture model that maximizes the likelihood over ground-truth occupied voxels in a feed-forward manner. To enable end-to-end training of networks and voxelization of a standard mixture model, we define mixture weights as normalized primitive volumes to implicitly enforce simplex constraints and derive novel voxelization formulas. Based on FLM, our FLM-Occ, a novel method that is capable of relocating randomly initialized primitives over long distances to model a scene. On Occ-ScanNet, FLM-Occ achieves superior accuracy using only 32 superquadrics, 2.7% of the prior SoTA, while running 3.7 times faster.
