DepthMap¶
Note
This node requires CUDA
Description
Retrieves the depth value of each pixel for all cameras that have been resolved by SfM.
settings
Name |
Description |
|---|---|
|
SfMData file. |
Images Folder |
Use images from a specific folder instead of those specify in the SfMData file. Filename should be the image uid. |
Downscale |
Image downscale factor (1, 2, 4, 8, 16) |
Min View Angle |
Minimum angle between two views.(0.0 - 10.0) |
Max View Angle |
Maximum angle between two views. (10.0 - 120.0) |
SGM: Nb Neighbour Cameras |
Semi Global Matching: Number of neighbour cameras (1 - 100) |
SGM: WSH: Semi Global Matching |
Half-size of the patch used to compute the similarity (1 - 20) |
SGM: GammaC |
Semi Global Matching: GammaC Threshold (0 - 30) |
SGM: GammaP |
Semi Global Matching: GammaP Threshold (0 - 30) |
Refine: Number of samples |
(1 - 500) |
Refine: Number of Depths |
(1 - 100) |
Refine: Number of Iterations |
(1 - 500) |
Refine: Nb Neighbour Cameras |
Refine: Number of neighbour cameras. (1 - 20) |
Refine: WSH |
Refine: Half-size of the patch used to compute the similarity. (1 - 20) |
Refine: Sigma |
Refine: Sigma Threshold (0 - 30) |
Refine: GammaC |
Refine: GammaC Threshold. (0 - 30) |
Refine: GammaP |
Refine: GammaP threshold. (0 - 30) |
Refine: Tc or Rc pixel size |
Use minimum pixel size of neighbour cameras (Tc) or current camera pixel size (Rc) |
Verbose Level |
verbosity level (fatal, error, warning, info, debug, trace) |
Output |
Output folder for generated depth maps |
default:
Detailed description
For all cameras that have been resolved by SfM, we want to retrieve the depth value of each pixel. Many approaches exist, like Block Matching, Semi-Global Matching (SGM) [Hirschmüller2005], [Hirschmüller2008] or ADCensus [Xing2011]. We will focus on the SGM method implemented in AliceVision.
For each image, we select the N best/closest cameras around. We select fronto-parallel planes based on the intersection of the optical axis with the pixels of the selected neighboring cameras. This creates a volume W, H, Z with many depth candidates per pixel. We estimate the similarity for all of them. The similarity is computed by the Zero Mean Normalized Cross-Correlation (ZNCC) of a small patch in the main image reprojected into the other camera. This create a volume of similarities. For each neighboring image, we accumulate similarities into this volume. This volume is very noisy. We apply a filtering step along X and Y axes which accumulates local costs which drastically reduce the score of isolated high values. We finally select the local minima and replace the selected plane index with the depth value stored into a depth map. This depth map has banding artifacts as it is based on the original selection of depth values. So a refine step is applied to get depth values with sub-pixel accuracy.
All these depth maps can be computed independently in parallel. Then we apply a filtering step to ensure consistency between multiple cameras. A compromise is chosen based on both similarity value and the number of coherent cameras to keep weakly supported surfaces without adding artefacts.
[Hirschmüller2005] |
Accurate and efficient stereo processing by semi-global matching and mutual information, H. Hirschmüller. CVPR 2005 |
[Hirschmüller2008] |
Stereo processing by semiglobal matching and mutual information, H. Hirschmüller, 2008 |
[Strecha2006] |
Combined depth and outlier estimation in multi-view stereo, C. Strecha, R. Fransens, and L. Van Gool, CVPR 2006 |
[Scharstein2002] |
A taxonomy and evaluation of dense two-frame stereo correspondence algorithms, D. Scharstein and R. Szeliski, 2002 |
[Xing2011] |
On building an accurate stereo matching system on graphics hardware. Xing, M., Xun, S., Mingcai Z., Shaohui J., Haitao, W., Xiaopeng Z., 2011 |