Awesome Depth Completion

Table of Contents

• About Sparse-to-Dense Depth Completion
• Current State of Depth Completion
  • Unsupervised VOID Benchmark
  • Supervised VOID Benchmark
  • Unsupervised KITTI Benchmark
  • Supervised KITTI Benchmark

About Sparse-to-Dense Depth Completion

In the sparse-to-dense depth completion problem, one wants to infer the dense depth map of a 3-D scene given an RGB image and its corresponding sparse reconstruction in the form of a sparse depth map obtained either from computational methods such as SfM (Strcuture-from-Motion) or active sensors such as lidar or structured light sensors.

Example 1: VOID dataset (indoor VIO)

Input RGB image	Sparse point cloud	Output point cloud from KBNet

Example 2: KITTI dataset (outdoor lidar)

Input RGB image	Output point cloud from ScaffNet

Current State of Depth Completion Methods

Here we compile both unsupervised/self-supervised (monocular and stereo) and supervised methods published in recent conferences and journals on the VOID (Wong et. al., 2020) and KITTI (Uhrig et. al., 2017) depth completion benchmarks. Our ranking considers all four metrics rather than just RMSE.

Quick Links

• Unsupervised VOID Benchmark
• Supervised VOID Benchmark
• Unsupervised KITTI Benchmark
• Supervised KITTI Benchmark

Unsupervised VOID Depth Completion Benchmark

Supervised VOID Depth Completion Benchmark

Unsupervised KITTI Depth Completion Benchmark

Supervised KITTI Depth Completion Benchmark