  Monte Carlo path tracer renders noisy image sequences at low sampling counts.
Although great progress has been made on denoising such sequences, existing
methods still suffer from spatial and temporary artifacts. In this paper, we
tackle the problems in Monte Carlo rendering by proposing a two-stage denoiser
based on the adaptive sampling strategy. In the first stage, concurrent to
adjusting samples per pixel (spp) on-the-fly, we reuse the computations to
generate extra denoising kernels applying on the adaptively rendered image.
Rather than a direct prediction of pixel-wise kernels, we save the overhead
complexity by interpolating such kernels from a public kernel pool, which can
be dynamically updated to fit input signals. In the second stage, we design the
position-aware pooling and semantic alignment operators to improve
spatial-temporal stability. Our method was first benchmarked on 10 synthesized
scenes rendered from the Mitsuba renderer and then validated on 3 additional
scenes rendered from our self-built RTX-based renderer. Our method outperforms
state-of-the-art counterparts in terms of both numerical error and visual
quality.
