臺灣博碩士論文加值系統

三維物件偵測是自動駕駛與智慧感知系統中的關鍵任務，尤其在Pedestrian與Cyclist等小型目標的辨識上，現有技術仍面臨精度與穩定性不足的挑戰。為此，本研究命名為 LION++ 三維物件偵測架構，針對稀疏點雲中特徵難以捕捉的問題進行優化，特別聚焦於提升小型物件的偵測性能與系統整體效率。
研究核心圍繞三項關鍵技術創新展開：調整 Z 軸視窗粒度，改善空間解析能力而不犧牲推理效率；設計自適應軸方向特徵門控（AAFG）模組，以動態調整 X、Y 軸的特徵表達強度，強化語意選擇性；引入HST-Diff模組，整合時間漸進擴散、層級區分、特徵繼承與空間密度優化等策略，協調語意穩定性與推理速度。
實驗以 KITTI 驗證集為基礎，實驗結果表明 LION++ 在整體 mAP 上自基準 67.34% 提升至 69.19%，並於Pedestrian類別（Moderate 階段）3D AP 提升達 3.91%。效率方面，完整版達FPS 62.5，推理優化版（LION++ Fast）進一步提升至FPS 68.03，且 mAP 仍維持在 68.22%，證明其具備即時應用潛力。
消融實驗亦確認三大模組具備明確的獨立效益與協同效應，同時對比多模態融合與傳統增強策略，實證 LION++ 之針對性設計在穩定性與準確性上更具優勢。
本研究展示了一個兼顧精度與效率的三維偵測架構，並為小型目標識別提供了有效解決方案。研究成果可應用於自動駕駛、邊緣運算與智慧監控等場域，未來將進一步擴展至多樣化氣候與跨域資料下的應用驗證。

With a focus on small-scale targets, specifically cyclists and pedestrians in sparse point clouds, this paper presents LION++, a novel 3D object detection architecture. Due to their limited point density, intricate shapes, and frequent occlusion, these non-rigid objects continue to present difficulties.
LION++ uses three significant innovations to address these issues. First, direction-aware semantic weighting is introduced by adaptive axial feature gating (AAFG). Second, the resolution was improved without sacrificing efficiency by fine-tuning the Z-axis window granularity from [32,16,8,4] to [16,8,4,2]. Third, we suggest a hierarchical spatio-temporal diffusion module called HST-Diff, which successfully strikes a balance between runtime speed and accuracy.
When evaluated on the KITTI validation set, LION++ achieved a notable mAP improvement from 67.34% to 69.19%. In particular, 3D AP for pedestrians (Moderate) rose from 54.19% to 58.10%, enhancing safety-critical detection. The inference-optimized LION++ Fast runs at 68.03 FPS with a minor mAP drop to 68.22%, while the full model operates at 62.5 FPS. These results, along with ablation studies and comparisons with multi-modal baselines, confirm LION++ as an effective and focused single-modality solution for tasks such as smart surveillance and autonomous driving.

摘要 ...i
Abstract ...ii
誌謝 ...iii
目錄 ...iv
表目錄 ...viii
圖目錄 ...x
第一章 緒論 ...1
1.1 研究背景 ...1
1.2 研究動機與目的 ...3
1.3 論文架構 ...4
第二章 相關研究 ...5
2.1 關於點雲 ...5
2.1.1 點雲數據格式與獲取方式 ...6
2.1.2 點雲處理挑戰 ...11
2.2 文獻回顧 ...12
2.2.1 基於點雲的物件偵測方法 ...12
2.2.2 基於體素的物件偵測方法 ...13
2.2.3 小物件與遠距離物體的偵測挑戰 ...14
2.3 相關技術 ...16
2.3.1 Mamba 狀態空間模型 ...16
2.3.2 軸向特徵處理 ...26
2.3.3 自適應擴散策略 ...28
第三章 LION ++：三維物件偵測強化方法 ...29
3.1 原始網路架構 ...29
3.1.1 SECOND 網路架構 ...29
3.1.2 LION 網路架構 ...33
3.2 基於自適應軸方向特徵門控之三維物件偵測強化方法 ...36
3.2.1 多尺度稀疏視窗口形狀設計 ...38
3.2.2 線性序列建模器選擇：Mamba ...41
3.2.3 自適應軸方向特徵門控（AAFG） ...42
3.2.4 架構整合與模組協同運作 ...49
第四章 結合分層時空擴散機制之三維物件偵測強化方法 ...51
4.1 架構設計理念 ...51
4.1.1 階層化設計理念 ...53
4.1.2 三維核心（層級-空間-時間）協同機制 ...54
4.1.3 與傳統擴散方法的比較優勢 ...55
4.2 層級階層設計（Hierarchical） ...56
4.2.1 層級感知擴散參數調控 ...56
4.2.2 層級間特徵傳遞 ...58
4.3 空間階層設計（Spatial） ...59
4.3.1 前景體素選擇 ...59
4.3.2 多尺度空間區域劃分 ...60
4.3.4 方向性擴散生成 ...65
4.4 時間階層設計（Temporal） ...66
4.4.1 訓練時間進程階段劃分 ...66
4.4.2 多階段訓練節奏與擴散規劃 ...71
4.5 HST-Diff擴散控制模型 ...72
4.6 HST-Diff協同效應分析 ...74
第五章 實驗結果與分析 ...76
5.1 實驗設置 ...76
5.1.1 KITTI數據集介紹 ...76
5.1.2 評估標準與指標 ...79
5.1.3 環境配置與參數設定 ...81
5.2 實驗結果與探討 ...82
5.2.1 整體性能概況 ...82
5.2.2 三維物件偵測性能分析 ...83
5.2.3 定性分析 ...92
5.3 消融實驗 ...100
5.3.1 核心組件效果驗證 ...100
5.3.2 LION++ 架構的組合驗證 ...106
5.3.3 LION++ 特徵定性分析 ...108
5.3.4 HST-Diff模組的構建 ...118
5.3.5 技術路線選擇的合理性驗證 ...120
第六章 結論與未來展望 ...124
6.1 研究結論 ...124
6.2 研究限制 ...125
6.3 未來研究方向 ...125
參考文獻 ...126
附錄一 ...131
Extended Abstract ...133
Chapter 1 Introduction ...134
Chapter 2 Related Work ...134
Chapter 3 LION++: Enhanced Method for 3D Object ...135
Chapter 4 For Enhancing 3D Object Detection via Hierarchical Spatio‑Temporal Diffusion ...136
Chapter 5 Experimental results and analysis ...137
Chapter 6 Conclusions ...139
Reference ...139

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