Abstract Transient rendering simulates light in motion, measuring the time of flight from the light source to the camera. However, the stochastic nature of Monte Carlo is aggravated in transient rendering, since samples are now spread along the temporal domain. In our work, we propose to denoise transient Monte Carlo renders by exploiting the spatio‐temporal correlation of transient light transport, extending a recent statistical denoising formulation. By relying on statistics, we achieve a near‐optimal tradeoff between reduced variance and introduced bias. We efficiently collect per‐time‐bin statistics in the temporal domain while avoiding impractical memory requirements, and use these collected statistics to analyze the spatio‐temporal correlation and discriminate which time bins should be combined. Our statistics‐based transient denoiser does not hallucinate, guarantees convergence of the result, is efficient, does not require any training and naturally handles participating media. We believe that the generality of our method might pave the way for denoising time‐resolved Monte Carlo simulations in other domains, such as non‐line‐of‐sight imaging, acoustic rendering, or absorption microscopy.

Holographic near-eye displays promise unparalleled depth cues, high-resolution imagery, and realistic three-dimensional parallax at a compact form factor, making them promising candidates for emerging augmented and virtual reality systems. However, existing holographic display methods often assume ideal viewing conditions and overlook real-world factors such as eye floaters and eyelashes—obstructions that can severely degrade perceived image quality. In this work, we propose a new metric that quantifies hologram resilience to artifacts and apply it to computer generated holography (CGH) optimization. We call this Artifact Resilient Holography (ARH). We begin by introducing a simulation method that models the effects of pre- and post-pupil obstructions on holographic displays. Our analysis reveals that eyebox regions dominated by low frequencies—produced especially by the smooth-phase holograms broadly adopted in recent holography work—are vulnerable to visual degradation from dynamic obstructions such as floaters and eyelashes. In contrast, random phase holograms spread energy more uniformly across the eyebox spectrum, enabling them to diffract around obstructions without producing prominent artifacts. By characterizing a random phase eyebox using the Rayleigh Distribution, we derive a differentiable metric in the eyebox domain. We then apply this metric to train a real-time neural network-based phase generator, enabling it to produce artifact-resilient 3D holograms that preserve visual fidelity across a range of practical viewing conditions—enhancing both robustness and user interactivity.

introducing a novel inversion method that leverages directionality information of polarized measurements to reduce directional ambiguities in the reconstruction.Our method is capable of imaging features of hidden surfaces inside the missing cone space of state-of-the-art NLOS methods, yielding fine reconstruction details even when using a fraction of measured points on the relay surface.We demonstrate the benefits of our method in both simulated and experimental scenarios.