3D object reconstruction is important for semantic scene understanding. It is
challenging to reconstruct detailed 3D shapes from monocular images directly
due to a lack of depth information, occlusion and noise. Most current methods
generate deterministic object models without any awareness of the uncertainty
of the reconstruction. We tackle this problem by leveraging a neural object
representation which learns an object shape distribution from large dataset of
3d object models and maps it into a latent space. We propose a method to model
uncertainty as part of the representation and define an uncertainty-aware
encoder which generates latent codes with uncertainty directly from individual
input images. Further, we propose a method to propagate the uncertainty in the
latent code to SDF values and generate a 3d object mesh with local uncertainty
for each mesh component. Finally, we propose an incremental fusion method under
a Bayesian framework to fuse the latent codes from multi-view observations. We
evaluate the system in both synthetic and real datasets to demonstrate the
effectiveness of uncertainty-based fusion to improve 3D object reconstruction
accuracy.

通过神经对象表示学习对象形状分布并将其映射到潜在空间，我们利用不确定性感知编码器直接从单个输入图像生成具有不确定性的潜在代码以模拟 3D 物体重建领域中的不确定性，并在贝叶斯框架下提出增量融合方法来融合多视角观测的潜在编码，从而提高 3D 对象重建的准确性。