CRISP: Fixing Flying Pixels in Latent LiDAR Generation via Diffusion Decoding

arXiv:2610.11376 · cs.CV, cs.AI · Submitted 2026-10-08 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-10-08

Updated: 2026-10-08

Code: https://github.com/ThibaultGROUEIX/ChamferDistancePytorch

Terminology

• Ablation — 17× in this paper · explained in 1 episode
• World Model — 16× in this paper · explained in 7 episodes
• Transformer — 14× in this paper · explained in 2 episodes
• Diffusion Model — 9× in this paper · explained in 8 episodes
• Fidelity — 9× in this paper · explained in 2 episodes
• Core — 8× in this paper · explained in 2 episodes
• Diffusion Models — 8× in this paper · explained in 6 episodes
• Generative Model — 8× in this paper · explained in 2 episodes
• Adaptation — 7× in this paper · explained in 1 episode
• Alignment — 6× in this paper · explained in 1 episode
• Decode — 6× in this paper · explained in 2 episodes
• Distillation — 5× in this paper · explained in 2 episodes
• Failure Modes — 5× in this paper · explained in 3 episodes
• Generative Models — 5× in this paper · explained in 2 episodes
• Provenance — 5× in this paper · explained in 4 episodes
• Benchmark — 4× in this paper · explained in 2 episodes
• Chamfer Distance — 4× in this paper
• Fine-Tuning — 4× in this paper · explained in 5 episodes
• Point Clouds — 4× in this paper · explained in 2 episodes
• Latent Representation — 3× in this paper · explained in 2 episodes
• Modality Gap — 3× in this paper · explained in 4 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Generative Modeling — 2× in this paper · explained in 3 episodes
• IEEE International Conference on Robotics and Automation — 2× in this paper
• Institutional Review Board — 2× in this paper
• Latent Diffusion Model — 2× in this paper · explained in 2 episodes
• Latent Diffusion Models — 2× in this paper · explained in 2 episodes
• Latent Dynamics — 2× in this paper · explained in 2 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Diffusion Process — 1× in this paper · explained in 2 episodes
• Domain Shift — 1× in this paper · explained in 4 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• Jensen-Shannon Divergence — 1× in this paper
• Latent Space — 1× in this paper · explained in 7 episodes
• Latent World Models — 1× in this paper · explained in 2 episodes
• Minimum Matching Distance — 1× in this paper
• Nash Equilibrium — 1× in this paper · explained in 2 episodes
• Over-smoothing — 1× in this paper · explained in 4 episodes
• Reconstruction Error — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• Sim-to-Real Gap — 1× in this paper · explained in 6 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Tokenization — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Variational Bayes — 1× in this paper · explained in 2 episodes

Sources

• Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets
• A Survey of World Models for Autonomous Driving
• Taming Transformers for Realistic Lidar Point Cloud Generation
• Gaussian Error Linear Units (GELUs)
• Auto-Encoding Variational Bayes
• LOGen: Toward Lidar Object Generation by Point Diffusion
• 3D and 4D World Modeling: A Survey
• WorldLens: Full-Spectrum Evaluations of Driving World Models in Real World
• L3DR: 3D-aware LiDAR Diffusion and Rectification
• PiD: Fast and High-Resolution Latent Decoding with Pixel Diffusion
• Physically Based Neural LiDAR Resimulation
• Adverse Weather Conditions Augmentation of LiDAR Scenes with Latent Diffusion Models
• Progressive Distillation for Fast Sampling of Diffusion Models
• R3DPA: Leveraging 3D Representation Alignment and RGB Pretrained Priors for LiDAR Scene Generation
• Score-Based Generative Modeling through Stochastic Differential Equations
• Range-Edit: Semantic Mask Guided Outdoor LiDAR Scene Editing
• Wan: Open and Advanced Large-Scale Video Generative Models
• Forging Spatial Intelligence: A Roadmap of Multi-Modal Data Pre-Training for Autonomous Systems
• Pixel-Perfect Depth with Semantics-Prompted Diffusion Transformers
• U4D: Uncertainty-Aware 4D World Modeling from LiDAR Sequences

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