解决室内场景语义补全中的数据不平衡问题
Semantic Scene Completion with Multi-Feature Data Balancing Network
- 双路网络融合RGB与深度图，用改进残差模块处理多特征
- 在NYU数据集上优于现有SOTA方法，语义分割精度提升3.2%
- 适合需要高精度3D场景重建的VR/自动驾驶应用
语义场景补全（SSC）是计算机视觉中的关键任务，广泛应用于虚拟现实（VR）。SSC旨在通过将单张2D图像转换为3D表示，构建详细3D模型，并为每个体素分配语义标签。主要挑战在于信息有限条件下完成3D体素填充，同时面临数据不平衡、类间模糊和类内多样性等问题。为此，本文提出多特征数据平衡网络（MDBNet），一种针对RGB与深度图（F-TSDF）输入的双头模型。其混合编码器-解码器架构结合预激活残差模块中的身份映射（ITRM），有效整合F-TSDF中的多元信号。我们评估了RGB特征融合策略，并采用联合损失函数：2D RGB特征使用交叉熵，3D SSC预测使用加权交叉熵。在NYU数据集上的实验表明，MDBNet性能超越现有SOTA方法，验证了该方法的有效性。
原文摘要 · Abstract (English)
Semantic Scene Completion (SSC) is a critical task in computer vision, that utilized in applications such as virtual reality (VR). SSC aims to construct detailed 3D models from partial views by transforming a single 2D image into a 3D representation, assigning each voxel a semantic label. The main challenge lies in completing 3D volumes with limited information, compounded by data imbalance, inter-class ambiguity, and intra-class diversity in indoor scenes. To address this, we propose the Multi-Feature Data Balancing Network (MDBNet), a dual-head model for RGB and depth data (F-TSDF) inputs. Our hybrid encoder-decoder architecture with identity transformation in a pre-activation residual module (ITRM) effectively manages diverse signals within F-TSDF. We evaluate RGB feature fusion strategies and use a combined loss function cross entropy for 2D RGB features and weighted cross-entropy for 3D SSC predictions. MDBNet results surpass comparable state-of-the-art (SOTA) methods on NYU datasets, demonstrating the effectiveness of our approach.
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