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版本更新 · v4arXiv:2505.05848

RefRef: A Dataset and Benchmark for Reconstructing Refractive and Reflective Objects

Yue Yin, Enze Tao, Weijian Deng, Dylan Campbell

原文摘要

Modern 3D reconstruction and novel view synthesis approaches have demonstrated strong performance on scenes with opaque, non-refractive objects. However, most assume straight light paths and therefore cannot properly handle refractive and reflective materials. The lack of datasets specialized for these effects has impeded efforts to fairly and thoroughly evaluate performance and thereby make progress in this domain. Most existing datasets focus on opaque scenes, while those targeting refractive objects are often limited to thin glass with negligible ray bending, untinted single-material objects, or backgrounds treated as infinitely-distant, leaving key failure modes under strong refraction untested. To address this gap, we introduce the RefRef dataset. It contains 150 synthetic and 60 real scenes spanning varying levels of geometric complexity and diverse background types that expose the limitations of existing methods. For the purpose of benchmarking, we also provide an oracle method that, given the object geometry and refractive indices, calculates accurate light paths for neural rendering, and a simple two-stage baseline that relaxes these assumptions. We evaluate these against state-of-the-art methods and show that the task is far from solved.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.39913

TokenRepel: Generating Diverse Image Sets Without Compromising Quality

Qingtao Yu, Zhen Wang, Ning Lai, Boyang Hu, Hongdong Li, Dylan Campbell

原文摘要

We present an inference-time approach that enhances the visual diversity of image sets generated by text-to-image models, without compromising the average image quality. Our method leverages the intrinsic geometry of hidden representations in pretrained models to diversify images generated from the same prompt. First, we introduce a token repulsion mechanism that increases the angle between feature tokens across samples. It takes a negative Riemannian gradient step on a von Mises-Fisher mixture distribution of tokens on the unit hypersphere. Second, to preserve the quality of each output image, we propose a token density preservation mechanism that projects the repulsion-induced prediction change onto a half-space aligned with the model's score direction. This encourages the updates to remain within the high-density regions learned by the generative model. We further extend this mechanism to the output of each intermediate layer, enabling finer-grained iterative guidance. We evaluate our method on three types of pretrained text-to-image models. Experiments show substantial gains in visual diversity under identical prompts, while maintaining a comparable level of image fidelity.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2603.16016

FlatLands: Indoor floormap completion from a single egocentric view

Subhransu S. Bhattacharjee, Dylan Campbell, Rahul Shome

原文摘要

A single egocentric image typically captures only a small portion of the floor, yet a complete metric traversability map of the surroundings would better serve applications such as indoor navigation. We introduce FlatLands, a dataset and benchmark for single-view indoor floor-map completion in metric bird's-eye view (BEV), evaluating eleven models: both deterministic and generative. FlatLands contain 270,575 observations from 17,656 scene records across six metric 3D datasets, with aligned observation, visibility, validity, and ground-truth maps and both in- and out-of-distribution protocols. We compare training-free, deterministic, ensemble, and generative methods on shared geometry-derived inputs and, without fine-tuning, on maps estimated from real egocentric RGB images. Our results show that predicting floor almost everywhere can achieve high IoU when most evaluated cells are floor, while generative sample sets outperform the best single-output model under a proper scoring rule and support safer risk-weighted planning. FlatLands establishes a rigorous testbed for evaluating both floor-map accuracy and how effectively alternative completions represent the ambiguity of unseen space.

cs.CV · cs.AI · cs.RO · eess.IV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02551

Flat Features, Detailed Images? Simple Detail Control for Diffusion Transformers

Qingtao Yu, Zhen Wang, Jiyang Zheng, Minghao Sun, Hongdong Li, Dylan Campbell

原文摘要

Prompt engineering is commonly used to encourage text-to-image models to generate more detailed images. However, it does not provide reliable, continuous, or disentangled control. We analyze feature tokens produced with and without detail-enhancing prompts across six diffusion transformers and observe a common pattern: these prompts tend to increase the entropy of each token's energy distribution while largely preserving its ordering. That is, flattening the distribution increases the level of detail. Motivated by this counter-intuitive finding, we introduce a simple training-free approach that continuously adjusts the level of detail according to a scalar. Specifically, it shifts the squared channel magnitudes of each token toward a higher entropy distribution with a time-aware scheduler to concentrate the intervention at intermediate sampling steps. Across extensive experiments on different types of diffusion transformers, we show that our approach provides continuous control over detail metrics while largely preserving image quality and semantic structure. It requires only a few lines of code and adds negligible inference time overhead.

cs.CV

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arXiv ↗PDF ↗