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surface normal loss

时间: 2023-10-05 13:12:18 浏览: 77

test_surface_test_

package io.vec.demo.mediacodec;import java.io.IOException;import java.nio.ByteBuffer;import android.app.Activity;import android.media.MediaCodec;import android.media.MediaCodec.BufferInfo;import android.media.MediaCodecInfo;import android.media.MediaExtractor;import android.media.MediaFormat;import android.os.Bundle;import android.os.Environment;import android.util.Log;import android.view.Surface;import android.view.SurfaceHolder;import android.view.SurfaceView;public class DecodeActivity extends Activity implements SurfaceHolder.Callback { // private static final String strVideo = // Environment.getExternalStorageDirectory() + "/test/xx.avi"; // private static final String strVideo = // Environment.getExternalStorageDirectory() + "/test/test.wmv"; // private static final String strVideo = // Environment.getExternalStorageDirectory() + // "/netease/cloudmusic/Music/Alborada Del Inka - Vientos suaves.mp3";

Surface normal loss is a type of loss function used in computer vision tasks such as semantic segmentation and depth estimation. It is used to ensure that the predicted surface normals of an image or a region of an image align with the ground truth surface normals. The surface normal loss is calculated as the cosine similarity between the predicted and ground truth surface normals. The surface normal loss can be formulated as: L = 1 - cos(theta) where theta is the angle between the predicted and ground truth surface normals. The loss value is minimized when the predicted and ground truth surface normals are aligned, and it is maximized when they are orthogonal. The surface normal loss can help improve the accuracy of computer vision models by encouraging the prediction of more accurate surface normals.

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