  Learning neural implicit representations has achieved remarkable performance
in 3D reconstruction from multi-view images. Current methods use volume
rendering to render implicit representations into either RGB or depth images
that are supervised by multi-view ground truth. However, rendering a view each
time suffers from incomplete depth at holes and unawareness of occluded
structures from the depth supervision, which severely affects the accuracy of
geometry inference via volume rendering. To resolve this issue, we propose to
learn neural implicit representations from multi-view RGBD images through
volume rendering with an attentive depth fusion prior. Our prior allows neural
networks to perceive coarse 3D structures from the Truncated Signed Distance
Function (TSDF) fused from all depth images available for rendering. The TSDF
enables accessing the missing depth at holes on one depth image and the
occluded parts that are invisible from the current view. By introducing a novel
attention mechanism, we allow neural networks to directly use the depth fusion
prior with the inferred occupancy as the learned implicit function. Our
attention mechanism works with either a one-time fused TSDF that represents a
whole scene or an incrementally fused TSDF that represents a partial scene in
the context of Simultaneous Localization and Mapping (SLAM). Our evaluations on
widely used benchmarks including synthetic and real-world scans show our
superiority over the latest neural implicit methods. Project page:
https://machineperceptionlab.github.io/Attentive_DF_Prior/
