Pith. sign in

REVIEW

State-of-the Art Motion Estimation in the Context of 3D TV

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1312.6497 v1 pith:6U55NC3F submitted 2013-12-23 cs.MM

State-of-the Art Motion Estimation in the Context of 3D TV

classification cs.MM
keywords motionacquisitionchaptercontentestimationextractionsceneshape
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
Share X Bluesky LinkedIn Reddit HN
read the original abstract

Progress in image sensors and computation power has fueled studies to improve acquisition, processing, and analysis of 3D streams along with 3D scenes/objects reconstruction. The role of motion compensation/motion estimation (MCME) in 3D TV from end-to-end user is investigated in this chapter. Motion vectors (MVs) are closely related to the concept of disparities, and they can help improving dynamic scene acquisition, content creation, 2D to 3D conversion, compression coding, decompression/decoding, scene rendering, error concealment, virtual/augmented reality handling, intelligent content retrieval, and displaying. Although there are different 3D shape extraction methods, this chapter focuses mostly on shape-from-motion (SfM) techniques due to their relevance to 3D TV. SfM extraction can restore 3D shape information from a single camera data.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.