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Manydepth: The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth

January 2022

tl;dr: Efficient and accurate use of multiframe information for monodepth.

Overall impression

For monodepth application, sequence information is often available at test time. There exists two ways to leverage multiframes for monodepth estimation. First uses expensive test-time refinement techniques (Consistent Video Depth, Robust CVD, CoMoDa, SSIA, etc) or recurrent network (). ManyDepth is an adaptive approach to dense depth estimation that can make use of seq info at test time when it is available.

The paper provides a good overview of recent advances of self-supervised monodepth.

ManyDepth address what was thought to be a forced choice in 3D reconstruction, between classic triangulation over multiple frames versus instant-but-fragile single-frame inference with a neural network. (Source)

Key ideas

  • Formulation
    • Train with many frames (both previous and future)
    • It does not rely on any semantic model
    • Can take in one or multiple frames during test time.
  • Builds on two well established components
    • Self-supervised reprojection based training (after SfM-Learner, Monodepth2)
      • Training with both t+1 and t-1 frames
      • Test with only current t frame
    • Multi-view cost volume
      • Set a series of depth plane $d \in P$, and set d_max and d_min
      • Source image t-1 is encoded into a feature F_t-1 (with dim H/4 x W/4 x C), warped into time t with hypothesized depth d and estimated pose from PoseNet.
      • For each d, L1 loss between warped feature and the feature F_t (with dim H/4 x W/4 x C), we get one slice of cost volume (with dim H/4 x W/4 x 1).
      • Build entire cost volume (with dim H/4 x W/4 x |P|).
      • Concat the cost volume with Ft into depth prediction encoder-decoder. (In monodepth, Ft only without cost volume)
  • Advantages/Innovations
    • Adaptive Cost Volumes
      • d_min and d_max are learned from training data, with the use of a running average. The running average is then used in test time. --> Like BN.
    • Moving objects: Use single view monodepth to supervise. Discard this network at test time.
      • Naively concatenating cost volume to the feature Ft leads to bad test time performance (overfitting)
      • Cost volume only works well in textured region in a static setting.
      • network may become over-reliant on the cost volume, and inherit the cost volume's mistakes
      • Use a single image depth network to regularize, but only in regions (within the "motion mask") where there is a large gap between multi- and single-image predictions.
    • Static scenes and start of sequence: Data augmentation by feeding identical images. --> This is similar to the training of CenterTrack.
      • Static scenes: during training, replacing the cost volume with a tensor of zeroes with probability of p (=0.25).
      • Start of sequence: replace I_t-1 input with It with probability of q (=0.25).
  • Architecture
    • $\theta_{pose}$: pose estimation
    • $\theta_{consistency}$: single-frame depth, disposable
    • $\theta_{depth}$: multi-frame depth

Technical details

  • TTR (test time refinement)
    • Multiple forward and backward passes is needed for each set. Several seconds to complete. --> This may be useful for offline perception.
    • One pass may be already effective and efficient (CoMoDA).
  • Recurrent approaches
    • Much more efficient compared to test-time-refinement.
    • Cons: They do not explicitly reason about geometry during inference. Inaccurate.
  • MVS (multiview stereo)
    • unordered image collection
    • ManyDepth is superior to MVS in that most MVS methods assume there is no moving objects in the scenes, and assumes that the camera is not static.

Notes