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.

Point cloud semantic segmentation is important for road scene perception, a task for driverless vehicles to achieve full fledged autonomy. In this work, we introduce Mask Point Transformer Network (MPT-Net), a novel architecture for point cloud segmentation that is simple to implement. MPT-Net consists of a local and global feature encoder and a transformer based decoder; a 3D Point-Voxel Convolution encoder backbone with voxel self attention to encode features and a Mask Point Transformer module to decode point features and segment the point cloud. Firstly, we introduce the novel MPT designed to specifically handle point cloud segmentation. MPT offers two benefits. It attends to every point in the point cloud using mask tokens to extract class specific features globally with cross attention, and provide inter-class feature information exchange using self attention on the learned mask tokens. Secondly, we design a backbone to use sparse point voxel convolutional blocks and a self attention block using transformers to learn local and global contextual features. We evaluate MPT-Net on large scale outdoor driving scene point cloud datasets, SemanticKITTI and nuScenes. Our experiments show that by replacing the standard segmentation head with MPT, MPT-Net achieves a state-of-the-art performance over our baseline approach by 3.8% in SemanticKITTI and is highly effective in detecting 'stuffs' in point cloud.