13 accepted papers
Generating trajectories from high-level commands is critical for autonomous driving, but prevailing methods suffer from a flaw we term semantic misalignment. By associating long trajectories with a single, static meta-action (e.g., “lane change”), these methods corrupt training data during maneuver
Modeling large-scale landscapes is a foundational yet time-consuming task in many 3D applications, typically requiring substantial expertise. Recently, Text-to-3D techniques have emerged as a promising, beginner-friendly prototyping approach for generating 3D content from textual input. However, ex
Offline reinforcement learning enables policy learning solely from fixed datasets, without costly or risky environment interactions, making it highly valuable for real-world applications. While Transformer-based approaches have recently demonstrated strong sequence modeling capabilities, they typica…
Recent advances in vision-language-action (VLA) models have motivated the extension of their capabilities to embodied settings, where reinforcement learning (RL) offers a principled way to optimize task success through interaction. However, existing methods remain fragmented, lacking both a unified …
Flicker artifacts in short-exposure images are caused by the interplay between the row-wise exposure mechanism of rolling shutter cameras and the temporal intensity variations of alternating current (AC)-powered lighting. These artifacts typically appear as uneven brightness distribution across the…
Large vision-language models (VLMs) have made great achievements in Earth vision. However, complex disaster scenes with diverse disaster types, geographic regions, and satellite sensors have posed new challenges for VLM applications. To fill this gap, we curate the first remote sensing vision-langua…
Lens flare occurs when shooting towards strong light sources, significantly degrading the visual quality of images. Due to the difficulty in capturing flare-corrupted and flare-free image pairs in the real world, existing datasets are typically synthesized in 2D by overlaying artificial flare templa…
Obtaining high-quality, practically usable 3D models of biological plants remains a significant challenge in computer vision and graphics. In this paper, we present a novel method for generating realistic 3D plant models from single-view photographs. Our approach employs a neural decomposition techn…
Recent advancements in solving large-scale traveling salesman problems (TSP) utilize the heatmap-guided Monte Carlo tree search (MCTS) paradigm, where machine learning (ML) models generate heatmaps, indicating the probability distribution of each edge being part of the optimal solution, to guide MCT…
The Optimal Reciprocal Collision Avoidance (ORCA) algorithm is widely used for modeling agents in collision avoidance scenarios. However, suffering from limitations such as the improper reciprocal assumption that each agent is supposed to take half the responsibility for collision avoidance, the per
Efficiently representing and reconstructing the 3D geometry of biological trees remains a challenging problem in computer vision and graphics. We propose a novel approach for generating realistic tree models from single-view photographs. We cast the 3D information inference problem to a semantic vox…
Hierarchical reinforcement learning (HRL) is a promising approach to solving long-term decision problems and complex tasks, as high-level policy can guide the training procedure of low-level policy with macro actions and intrinsic rewards. However, the amount that macro actions influence decision-ma…
Shadow removal is a computer-vision task that aims to restore the image content in shadow regions. While almost all recent shadow-removal methods require shadow-free images for training, in ECCV 2020 Le and Samaras introduces an innovative approach without this requirement by cropping patches with a…