PDC: Piecewise Depth Completion utilizing Superpixels
Depth completion from sparse LiDAR and high-resolution RGB data is one of the foundations for autonomous driving techniques. Current approaches often rely on CNN-based methods with several known drawbacks: flying pixel at depth discontinuities, overfitting to both a given data set as well as error metric, and many more. Thus, we propose our novel Piecewise Depth Completion (PDC), which works completely without deep learning. PDC segments the RGB image into superpixels corresponding the regions with similar depth value. Superpixels corresponding to same objects are gathered using a cost map. At the end, we receive detailed depth images with state of the art accuracy. In our evaluation, we can show both the influence of the individual proposed processing steps and the overall performance of our method on the challenging KITTI dataset.
Code (0)
등록된 구현이 없습니다.
Tasks
Autonomous DrivingDepth CompletionSuperpixelsSimilar Papers 제목 키워드 기반
Fast, Approximate Piecewise-Planar Modeling Based on Sparse Structure-from-Motion and Superpixels
State-of-the-art Multi-View Stereo (MVS) algorithms deliver dense depth maps or complex meshes with very high detail, and redundancy over regular surfaces. In turn, our interest lies in an approximate, but light-weight m…
SuperpixelsHierarchical Piecewise-Constant Super-regions
Recent applications in computer vision have come to heavily rely on superpixel over-segmentation as a pre-processing step for higher level vision tasks, such as object recognition, image labelling or image segmentation. …
DenoisingFormImage SegmentationObject Recognition+3Monocular Piecewise Depth Estimation in Dynamic Scenes by Exploiting Superpixel Relations
In this paper, we propose a novel and specially designed method for piecewise dense monocular depth estimation in dynamic scenes. We utilize spatial relations between neighboring superpixels to solve the inherent relativ…
Depth EstimationMonocular Depth EstimationOptical Flow EstimationSemantic Segmentation+1Discontinuous and Smooth Depth Completion with Binary Anisotropic Diffusion Tensor
We propose an unsupervised real-time dense depth completion from a sparse depth map guided by a single image. Our method generates a smooth depth map while preserving discontinuity between different objects. Our key idea…
Depth CompletionUnsupervised Depth Completion from Visual Inertial Odometry
We describe a method to infer dense depth from camera motion and sparse depth as estimated using a visual-inertial odometry system. Unlike other scenarios using point clouds from lidar or structured light sensors, we hav…
Depth Completion