---
id: wiki-2026-0508-깊이-지각-depth-perception
title: 깊이 지각(Depth perception)
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Depth Perception, Stereopsis, 입체시]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [perception, vision, vr, graphics, psychology]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: GLSL / C++
  framework: OpenGL / Vulkan / Unity
---

# 깊이 지각(Depth perception)

## 매 한 줄
> **"매 brain 의 multiple cues 의 fusion 의 3D world reconstruction."**. 매 binocular (stereopsis) + monocular (parallax, perspective, occlusion) cues 의 combination, 매 VR/AR 의 design 의 foundation, 매 stereo rendering / depth map 의 graphics 의 핵심.

## 매 핵심

### 매 binocular cues
- Stereopsis: 매 두 eye 의 disparity (~63mm IPD).
- Convergence: 매 eye muscles 의 toe-in angle.
- Effective range: 매 ~10m 까지.

### 매 monocular cues
- Motion parallax: 매 가까운 물체 의 빠른 이동.
- Linear perspective: 매 parallel lines 의 vanishing point.
- Occlusion: 매 앞 의 물체 의 뒤 의 물체 가림.
- Relative size / texture gradient / aerial perspective.
- Accommodation: 매 lens focus 의 muscle feedback.

### 매 응용
1. Stereoscopic 3D rendering (VR, 3D cinema).
2. Depth-based effects (DOF, fog, SSAO).
3. Computer vision depth estimation (MiDaS, Depth Anything v2).
4. Photogrammetry / 3D reconstruction.

## 💻 패턴

### Pattern 1 — Stereo camera setup (Unity)
```csharp
public class StereoCamera : MonoBehaviour {
    public float ipd = 0.063f; // 63mm
    public Camera leftCam, rightCam;
    void LateUpdate() {
        leftCam.transform.localPosition = new Vector3(-ipd/2, 0, 0);
        rightCam.transform.localPosition = new Vector3( ipd/2, 0, 0);
    }
}
```

### Pattern 2 — Anaglyph 3D shader (GLSL)
```glsl
// Red-cyan anaglyph
vec3 left  = texture(leftEye, uv).rgb;
vec3 right = texture(rightEye, uv).rgb;
fragColor = vec4(left.r, right.g, right.b, 1.0);
```

### Pattern 3 — Depth-from-stereo (block matching, OpenCV)
```cpp
auto stereo = cv::StereoBM::create(16, 15);
cv::Mat disparity;
stereo->compute(leftGray, rightGray, disparity);
// depth = baseline * focal / disparity
```

### Pattern 4 — Monocular depth estimation (Depth Anything v2)
```python
from transformers import pipeline
depth = pipeline("depth-estimation", model="depth-anything/Depth-Anything-V2-Large")
result = depth(image)  # returns depth map
```

### Pattern 5 — Vergence-accommodation conflict mitigation (varifocal HMD)
```cpp
// Track gaze depth, drive lens focal distance
float gazeDepth = eyeTracker.getVergenceDistance();
varifocalLens.setFocalDistance(gazeDepth);
```

### Pattern 6 — DOF (depth of field) post-process
```glsl
float depth = texture(depthTex, uv).r;
float blur = abs(depth - focusDepth) * dofStrength;
fragColor = mix(sharpColor, blurredColor, clamp(blur, 0, 1));
```

## 매 결정 기준
| 상황 | Approach |
|---|---|
| 매 VR rendering | Stereo cameras + IPD calibration |
| 매 single image depth | Depth Anything v2 / MiDaS |
| 매 dual camera depth | Stereo block matching / SGBM |
| 매 LiDAR available | Direct depth (no estimation) |
| 매 NeRF / Gaussian Splatting | Multi-view consistency loss |

**기본값**: 매 VR 의 stereoscopic + 매 single image 의 Depth Anything v2.

## 🔗 Graph
- 부모: [[Computer Vision|Computer-Vision]]
- 변형: [[Stereopsis]]
- 응용: [[가상현실(VR)]]
- Adjacent: [[Vergence-Accommodation-Conflict]]

## 🤖 LLM 활용
**언제**: 매 VR rendering pipeline 의 design, 매 depth cue 의 trade-off 분석, 매 monocular depth model 의 selection.
**언제 X**: 매 individual user 의 stereo blindness 의 diagnosis (medical), 매 hardware-specific IPD calibration.

## ❌ 안티패턴
- **Fixed IPD**: 매 user 마다 의 IPD 의 variation (55-72mm) 무시 → eye strain.
- **Excessive parallax**: 매 screen edge 의 reverse stereo → headache.
- **VAC (Vergence-Accommodation Conflict)**: 매 fixed-focal HMD 의 close objects 의 discomfort.
- **Monocular cue 만 의존**: 매 depth ambiguity 의 cause.

## 🧪 검증 / 중복
- Verified (Goldstein "Sensation and Perception", SIGGRAPH papers).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — depth cues + stereo rendering + monocular depth estimation |
