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Haotong Lin

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3 papers

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AAAI Conference 2026 Conference Paper

BAT: Learning Event-based Optical Flow with Bidirectional Adaptive Temporal Correlation

Gangwei Xu
Haotong Lin
Zhaoxing Zhang
Hongcheng Luo
Haiyang Sun
Xin Yang

Event cameras deliver visual information characterized by a high dynamic range and high temporal resolution, offering significant advantages in estimating optical flow for complex lighting conditions and fast-moving objects. Current advanced optical flow methods for event cameras largely adopt established image-based frameworks. However, the spatial sparsity of event data limits their performance. In this paper, we present BAT, an innovative framework that estimates event-based optical flow using bidirectional adaptive temporal correlation. BAT includes three novel designs: 1) a bidirectional temporal correlation that transforms bidirectional temporally dense motion cues into spatially dense ones, enabling accurate and spatially dense optical flow estimation; 2) an adaptive temporal sampling strategy for maintaining temporal consistency in correlation; 3) spatially adaptive temporal motion aggregation to efficiently and adaptively aggregate consistent target motion features into adjacent motion features while suppressing inconsistent ones. Our BAT achieves state-of-the-art performance on the DSEC-Flow benchmark, outperforming existing methods by a large margin while also exhibiting sharp edges and high-quality details. Our BAT can accurately predict future optical flow using only past events, significantly outperforming E-RAFT’s warm-start approach.

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NeurIPS Conference 2025 Conference Paper

Pixel-Perfect Depth with Semantics-Prompted Diffusion Transformers

Gangwei Xu
Haotong Lin
Hongcheng Luo
Xianqi Wang
JINGFENG YAO
Lianghui Zhu
Yuechuan Pu
Cheng Chi_

This paper presents Pixel-Perfect Depth, a monocular depth estimation model based on pixel-space diffusion generation that produces high-quality, flying-pixel-free point clouds from estimated depth maps. Current generative depth estimation models fine-tune Stable Diffusion and achieve impressive performance. However, they require a VAE to compress depth maps into the latent space, which inevitably introduces flying pixels at edges and details. Our model addresses this challenge by directly performing diffusion generation in the pixel space, avoiding VAE-induced artifacts. To overcome the high complexity associated with pixel-space generation, we introduce two novel designs: 1) Semantics-Prompted Diffusion Transformers ( SP-DiT ), which incorporate semantic representations from vision foundation models into DiT to prompt the diffusion process, thereby preserving global semantic consistency while enhancing fine-grained visual details; and 2) Cascade DiT Design that progressively increases the number of tokens to further enhance efficiency and accuracy. Our model achieves the best performance among all published generative models across five benchmarks, and significantly outperforms all other models in edge-aware point cloud evaluation. Project page: https: //pixel-perfect-depth. github. io/.

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IJCAI Conference 2022 Conference Paper

Learning to Estimate Object Poses without Real Image Annotations

Haotong Lin
Sida Peng
Zhize Zhou
Xiaowei Zhou

This paper presents a simple yet effective approach for learning 6DoF object poses without real image annotations. Previous methods have attempted to train pose estimators on synthetic data, but they do not generalize well to real images due to the sim-to-real domain gap and produce inaccurate pose estimates. We find that, in most cases, the synthetically trained pose estimators are able to provide reasonable initialization for depth-based pose refinement methods which yield accurate pose estimates. Motivated by this, we propose a novel learning framework, which utilizes the accurate results of depth-based pose refinement methods to supervise the RGB-based pose estimator. Our method significantly outperforms previous self-supervised methods on several benchmarks. Even compared with fully-supervised methods that use real annotated data, we achieve competitive results without using any real annotation. The code is available at https: //github. com/zju3dv/pvnet-depth-sup.

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