  Neural Radiance Field (NeRF) is a popular method in representing 3D scenes by
optimising a continuous volumetric scene function. Its large success which lies
in applying volumetric rendering (VR) is also its Achilles' heel in producing
view-dependent effects. As a consequence, glossy and transparent surfaces often
appear murky. A remedy to reduce these artefacts is to constrain this VR
equation by excluding volumes with back-facing normal. While this approach has
some success in rendering glossy surfaces, translucent objects are still poorly
represented. In this paper, we present an alternative to the physics-based VR
approach by introducing a self-attention-based framework on volumes along a
ray. In addition, inspired by modern game engines which utilise Light Probes to
store local lighting passing through the scene, we incorporate Learnable
Embeddings to capture view dependent effects within the scene. Our method,
which we call ABLE-NeRF, significantly reduces `blurry' glossy surfaces in
rendering and produces realistic translucent surfaces which lack in prior art.
In the Blender dataset, ABLE-NeRF achieves SOTA results and surpasses Ref-NeRF
in all 3 image quality metrics PSNR, SSIM, LPIPS.
