6583 papers
Fast-BEV++ is a vision-only Bird's-Eye-View perception framework that resolves the trade-off between accuracy and deployment efficiency by employing a hardware-oriented, kernel-free architecture to achieve a new state-of-the-art 0.488 NDS on nuScenes while delivering real-time inference at over 134 FPS.
Photo3D is a framework that advances photorealistic 3D generation by leveraging GPT-4o-Image data within a structure-aligned multi-view synthesis pipeline to create detail-enhanced datasets, thereby enabling realistic texture refinement while preserving geometric consistency across diverse 3D-native generators.
This paper introduces a modular, fully learning-based neural image signal processing (ISP) framework that offers unprecedented control over intermediate rendering stages to enhance scalability, generalization, and flexibility, enabling a user-interactive photo-editing tool capable of unlimited post-editable re-rendering with competitive performance across multiple test sets.
UniCoR is a novel self-supervised framework that addresses the challenges of insufficient semantic understanding, inefficient modality fusion, and weak cross-language generalization in hybrid code retrieval by employing multi-perspective supervised contrastive learning and representation distribution consistency, thereby achieving state-of-the-art performance on both empirical and large-scale benchmarks.
This paper introduces VTP, a unified pre-training framework that optimizes visual tokenizers through joint image-text contrastive, self-supervised, and reconstruction losses to shift the latent space focus from low-level pixel accuracy to high-level semantics, thereby solving the "pre-training scaling problem" and enabling significantly improved, compute-efficient generative performance.
This paper argues that achieving high-performance end-to-end data movement requires shifting focus from raw network bandwidth to a holistic hardware-software co-design approach, introducing the "Drainage Basin Pattern" to identify and resolve bottlenecks across six critical paradigms ranging from network latency to host-side factors.
This paper introduces SORS, a modular, high-fidelity simulator based on the finite element method and constrained nonlinear optimization that accurately models complex soft robot dynamics and contact interactions, effectively bridging the sim-to-real gap for prototyping and control optimization.
This paper introduces a lightweight, pretrained history encoder that efficiently compresses long video histories into short embeddings using a frame query objective, enabling content-consistent autoregressive video generation under limited compute and memory constraints.
This paper introduces Spatial4D-Bench, a large-scale, multi-task benchmark comprising approximately 40,000 question-answer pairs across 18 tasks and six cognitive categories, designed to comprehensively evaluate and reveal the current limitations of Multimodal Large Language Models in achieving human-level 4D spatial intelligence.
This paper presents VISO, a robust underwater SLAM system that fuses stereo cameras, IMUs, and 3D sonar with novel calibration and photometric rendering techniques to achieve accurate 6-DoF localization and real-time, high-fidelity dense 3D reconstruction in challenging aquatic environments.
The paper introduces FlyPose, a lightweight, real-time human pose estimation pipeline optimized for aerial UAV views that achieves significant accuracy improvements across multiple datasets and is successfully deployed on-board a quadrotor, accompanied by the release of a new challenging dataset called FlyPose-104.
This paper presents InsSo3D, a robust SLAM framework that fuses 3D sonar point clouds with Inertial Navigation System data to enable accurate, large-scale 3D mapping and drift correction for underwater inspections in turbid environments.
The paper introduces OnlineSI, a framework that enables Multimodal Large Language Models to continuously improve spatial understanding and grounding in dynamic environments by maintaining a finite spatial memory and integrating 3D point cloud data with semantic information.
This paper introduces SRA 2, a lightweight intrinsic guidance framework that accelerates diffusion transformer training and improves generation quality by aligning intermediate latent features with pre-trained VAE features via a simple projection layer, eliminating the need for external encoders or dual-model setups while incurring minimal computational overhead.
This paper addresses the inadequacy of traditional safety-case frameworks for modern AI systems by introducing comprehensive taxonomies of claims, arguments, and evidence, along with a reusable, structured template designed to construct credible and adaptive safety cases for generative and agentic AI.
The paper introduces FARTrack, a fast autoregressive visual tracking framework that leverages Task-Specific Self-Distillation and Inter-frame Autoregressive Sparsification to achieve real-time, high-performance tracking (70.6% AO on GOT-10k) with exceptional inference speeds of up to 343 FPS on GPU and 121 FPS on CPU.
The paper introduces SpatialReward, a reward model that leverages explicit spatial reasoning to overcome the "Attention Collapse" limitation in existing evaluators, thereby providing fine-grained, accurate signals that significantly enhance online reinforcement learning performance for image editing tasks.
The paper proposes (MGS)-Net, a geometry-grounded framework that unifies Micro-Geometric Scale Adaptation and Macro-Geometric Structure Filtering to overcome geometric misalignment and achieve state-of-the-art cross-view geo-localization performance.
The paper presents APEX, a deep reinforcement learning framework that enables a 29-DoF Unitree G1 humanoid robot to autonomously traverse platforms up to 114% of its leg length by composing perceptive climbing, walking, and reconfiguration skills through a novel ratchet progress reward and robust sim-to-real perception strategies.
This paper proposes RL-Co, a reinforcement learning-based sim-real co-training framework that combines supervised fine-tuning on mixed real and simulated data with interactive simulation fine-tuning anchored by real-world data, achieving significant improvements in real-world success rates, generalization, and data efficiency for Vision-Language-Action models.