用模拟器物理引擎生成点云，加速3D语义映射10倍以上。
Voxel-Aggregated Feature Synthesis: Efficient Dense Mapping for Simulated 3D Reasoning
- 基于模拟器物理引擎的点云，合成视图减少冗余计算
- 仅需对场景物体数嵌入特征，速度提升一个数量级
- 适合需要频繁修改环境的具身智能研究
针对当前先进开放集多模态3D映射（密集3D映射）算法计算开销激增的问题，本文提出体素聚合特征合成（VAFS），一种高效的仿真环境密集3D映射方法。密集3D映射需对连续的RGBD帧进行分割与嵌入并融合为3D结构，但帧间差异小导致大量重复计算，使该方法在需频繁修改环境的具身智能研究中不切实际。VAFS通过利用模拟器物理引擎已生成的分割点云，合成各区域视图，将需嵌入的特征数从捕获的RGBD帧数降至场景物体数，实现接近“真值”的语义地图构建，速度比传统方法快一个数量级。通过评估不同物体的语义查询交并比（IoU），验证VAFS在准确率与速度上均优于现有技术。
原文摘要 · Abstract (English)
We address the issue of the exploding computational requirements of recent State-of-the-art (SOTA) open set multimodel 3D mapping (dense 3D mapping) algorithms and present Voxel-Aggregated Feature Synthesis (VAFS), a novel approach to dense 3D mapping in simulation. Dense 3D mapping involves segmenting and embedding sequential RGBD frames which are then fused into 3D. This leads to redundant computation as the differences between frames are small but all are individually segmented and embedded. This makes dense 3D mapping impractical for research involving embodied agents in which the environment, and thus the mapping, must be modified with regularity. VAFS drastically reduces this computation by using the segmented point cloud computed by a simulator's physics engine and synthesizing views of each region. This reduces the number of features to embed from the number of captured RGBD frames to the number of objects in the scene, effectively allowing a "ground truth" semantic map to be computed an order of magnitude faster than traditional methods. We test the resulting representation by assessing the IoU scores of semantic queries for different objects in the simulated scene, and find that VAFS exceeds the accuracy and speed of prior dense 3D mapping techniques.
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