December 2019
tl;dr: Use sparse density measurement from VO algorithm to enhance depth estimation.
Overall impression
This paper combines the idea of depth estimation with depth completion.
Key ideas
- The paper used a sparsity invariant autoencoder to densify the sparse measurement before concatenating the sparse data with RGB input.
- Inner Loss: between SD (sparse depth) and DD (denser depth after sparse conv)
- Outer loss: between SD and d (dense estimation) on where the SD is defined.
Technical details
- VO pipeline only provides ~0.06% of sparse depth measurement.
- Sparcity invariant CNNs performs weighted average only on valid inputs. This makes the network invariant to input sparsity.
Notes
- Best supervised mono depth estimation: DORN
- Scale recovery method is needed for monodepth estimation and any mono VO methods.
- Both ORB-SLAM v1 and v2 supports mono and stereo.