PaperReading

monoDR: Monocular Differentiable Rendering for Self-Supervised 3D Object Detection

September 2020

tl;dr: Use differentiable rendering for monocular 3D object detection, without any 3D labels.

Overall impression

The gist of the paper is how to perform 3D object detection without 3D annotation. The answer is to use differentiable rendering to form self-supervised constraints with 2D annotations.

The scale ambiguity due to projective entanglement of depth and scale are handled by explicitly predicting the metric size of objects. monoDR uses a size loss penalizing the reconstructed loss from deviating too much of the averaged size of the object class. Concurrent work of SDFLabel uses lidar to recover the absolute scale.

It uses an analysis-by-synthesis methodology which is similar to 3D RCNN and RoI10D, and SDFLabel.

The paper also provides an interesting descent-and-explore approach to avoid local minima, most likely by using the so-called hindsight loss.

Key ideas

• Architecture predicts
  • location (XYZ), dimension (HWL), rotation (YPR)
  • shape (latent vector h_s)
  • texture (latent vector h_t)
• Differentiable Rendering via Neural 3D mesh renderer.
• Loss
  • Mask (silhouette) loss
  • bbox loss, allowing a margin up to t (loss with flat bottom)
  • distance loss (compare with packNet)
  • projection loss (photometric loss)
• Mask loss alone will be not very sensitive to rotation, leading to large estimation error to rotation.
• Shape encoder and decoder are trained from ShapeNet dataset, down to a 8-dim vector.
• 3D conf is 2D conf modulated by self-consistency. This improves performance.

Technical details

• Latent space of car shapes
  • 3D RCNN 10-dim
  • RoI10D 6-dim
  • monoDR 8-dim
• Only 2D annotation is not good enough. Even noisy supervion from depth is essential for 3D monocular object detection.
• Escaping local minima: Estimating rotation with render and compare approaches can easily lead to local minima. –> this is implemented as a “hindsight loss”, initially developed for multi-choice learning. It leverages the fact that the function min is a differentiable way to ensemble multiple differentiable choices.
  • Unsupervised Learning of Shape and Pose with Differentiable Point Cloud NIPS 2018
  • Multi-view Consistency as Supervisory Signal for Learning Shape and Pose Prediction CVPR 2018
• The bbox used in the work seems to be modal. The estimation of 3D objects need to be amodal to get worked properly.
• The paper argues that better segmentation results does not necessarily leads to better results. –> but why?

Notes

• Questions and notes on how to improve/revise the current work