別紙41(課程博士(英文))Date of submission (month day, year)
Department

July 5仙,2023

Of

Computer science
StudenHD Number

YasushiKanazawa

D 179301

and EngineeringSupervisor

Pplicant's name

AndiHendra

Abst"act (Doct0わ
Title ofThesis

Depth Estimation from A single lmage using Global structure and Local scene lnformation

Approx.80o words
Xve propose a novel framework for accurately estimating depth information from a single image. Despite its
Simplicity and compactness, our framework demonstrates consistent and reliable performance by e仟'ectively
integrating global and localimage features.

To address the cha11enge, we employ two distinct deep neural network architectures.、Ne adopt anencoder・decoder model with a txvo・stage strategy in the 6rst architecture. This approach leverages multi・task loss
Optimization and incorporates adaptive leaming rate adjustment based on the loss behaviour、 The second

architecture expands the conditional GAN (CGAN) model, introducing a three・player GAN (TP、GAN)介alnework.10 e血ance the reliabiliw of the depth estimation, we include the structural similarity measure(SSIM) 10ss as pad ofthis architecture. By utilizing this architecture, we aim to optimize the depth estimation
Performance.

Our proposed architectures utilize 1 χ I convolution to reduce the dimensionality of the feature maps,
thereby enabling the modelto focus on capturing high・1evel semantics and global context. conversely,10calfeatures are extracted through stacks ofconvolution with sma11er kemels relative to the input size that ca11help in
Capturing local context and details that m地ht be overlooked by global features alone. combining global and local
features enables the modelto leverage the overaⅡ Scene understanding and fine・8rained local details to enhance
the accuracy ofdepth prediction.

To evaluate the e丘ectiveness of our approaches, we conducted comprehensive quantitative andqua1北ative comparisons with several state・of・the‑art methods in the field. our experiments were conducted on
two we11・known publicly depth datasets, the indoor 卜IYU Depth V2 and outdoor KITTl datasets. The results
Consistently demonstrate that our proposed method outperforms numerous previous related monocular depth
Strate8ies. Despite the conciseness of our model architecture, it consistendy delivers reliable performance when
Compared to the transfonner‑based model,6.1rther demonstrating its e缶Cacy.
Furthermore, we investigated the generalization capabilities of our modelto other datasets. To assess
Cross・dataset adaptation, we trained our model on one dataset arld tested it on another and vice versa. our model

exhibits reHable generalization by effectively leaming scene variations across indoor and outdoor datasets.
Notably, when trained on indoor data and tested on the outdoor range dataset, our model achieved consistentPerformance ofsslM scores, which some values close to one.
In addition, we conducted a11in‑depth analysis to assess the robustness of our depth estimation model
Under di仟erent contrast levels.10 evaluate our model's perfomlance, we generated visualizations of estimateddepth and calculated the (SSIM) score using images captured under different contrast conditions. speci丘Ca11y, weevaluated six ra11dom KITTl data salnples containing scenes with normal,10wer, and higher contrast levels. The

results demonstrated that our model oU中剖formed other methods, indicating that the sslM metrics consistently
Showed superior performance across the dataset.

In future research,it is imperative to fU11her advance the single ilnage depth estimation fleld by focusing
On developing models that exhibit enh釘Iced generalization capabilities across diverse datasets. This could beachieved by designing a11 adaptive modelthat e行'ectively discriminates between ground truth and generated depth
and accurately classi6es whether the input image belongs to an indoor or outdoor dataset. such advancements、vould significanuy contribute to the robustness and versatility ofdepth estimation methods.