We present a flexible method for fusing information from optical and range sensors based on an accelerated highdimensional filtering approach. Our system takes as input a sequence of monocular camera images as well as a stream of sparse range measurements as obtained from a laser or other sensor system. In contrast with existing approaches, we do not assume that the depth and color data streams have the same data rates or that the observed scene is fully static. Our method produces a dense, high-resolution depth map of the scene, automatically generating confidence values for every interpolated depth point. We describe how to integrate priors on object motion and appearance and how to achieve an efficient implementation using parallel processing hardware such as GPUs.
Dolson et al. REF also used joint bilateral filter to upsample range data, but their method relies on a monocular image sequence and a stream of sparse range measurements to produces a dense, high-resolution dynamic depth map of the scene.
Upsampling range data in dynamic environments
2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Computer Science